Essays on World Mythology

1. The battle within the individual between the greater good of the community and personal desires is just about as old as time itself; the decision that Gilgamesh made to leave his kingdom unattended while he pursued the gift of eternal life for his friend, Enkidu, could have had disastrous consequences, but he decided to do it anyway. The Epic of Gilgamesh is one of the oldest works of literature that we still have today, and the same battles rage within us today. Many of the myths and tales that we have read show this to be true as well. In particular, the greed of Paris when tempted by Helen, the desire of King Menelaus for revenge and the desire of King Agamemnon for personal prestige brought tragedy on themselves and others in Homer’s Iliad, the telling of the Trojan War. According to all accounts, Helen was the most beautiful woman on the planet – even more beautiful than Aphrodite, at least in the opinion of Paris. While the resulting jealousy on Aphrodite’s part allegedly had a lot to do with the next decade of death for the Greeks and Trojans, the decisions that the major players made also were primary factors. First was Paris’ decision to take Helen away with him. Despite the fact that he had to know that war would ensue, the lust that Paris felt for Helen overwhelmed the other factors in the decision, and he spirited her away at the end of a diplomatic mission. King Menelaus, Helen’s (much older) husband, obviously took exception at what he viewed at the theft of his wife, and raised and army throughout the Argive lands to go and get his wife back. While one could argue that Menelaus would no longer have commanded the respect of his people if he had not gone after his wife, the fact remains that it was his choice to g ather the other Achaean kings for war, and it was his decision to chase Helen across the Aegean Sea. Agamemnon, Menelaus’ brother, also put personal desires over the good of others. When he had to give the young woman Khryseis back to her father, in order to appease the gods and get the winds going so that the Argive ships could head to Troy, he insists on getting the woman whom Achilles had claimed from their prior war, Briseis, as compensation, feeling that a king should not have to give up any privileges just to appease the gods. Achilles is so angry about this, though, that he refuses to fight on the Greek side well into the war, which means that there were many more casualties than there might have been otherwise. Agamenon’s selfishness extends the tragedy of Troy much longer than it needed to be extended. 2. A true hero must have an actual quest, and that quest must both be worthwhile and must allow the hero to prove himself or herself. The hero must show courage in accomplishing the quest as well. Odysseus, the central character in Homer’s Odyssey, thinks that he will be able to make his way back from Troy to Ithaca simply, but his refusal to acknowledge Poseidon’s role in helping the Greeks carry the battle means that he will spend the next 20 years fighting against Poseidon to cross the sea back to his home. Odysseus faces and passes many tests along the way, such as avoiding the trap at the island of the Sirens, evading the snare of Circe, defeating the Cyclops, and overcoming the loss of the Aeolian winds. Also, Odysseus grows in character over the course of the story, going from the brash braggart boasting from the ramparts of Troy to the proud yet humble man who, upon finally arriving on the shores of Ithaca, takes on the trappings of a beggar in order to return t o his former splendor on the throne. Lancelot had many heroic qualities, but he ultimately failed to meet the definition of a hero. He was brave, and he did complete many quests, but his ethical failing that results from his affair with Queen Guinevere keeps him from possessing the true title. An actual hero would have remained true to his king, no matter what the temptations; while heroes do exhibit flaws throughout literature, they ultimately overcome them. Instead, Lancelot is drawn into intrigue with Mordred to overthrow Arthur, and it is this treachery that keeps Lancelot from holding the title of a true hero. Many heroes in modern popular culture come from the world of sport. For many baseball fans, as well as former addicts, the professional baseball player Josh Hamilton fits the definition of a hero. He has great talents (which is often another sign of a hero), but his quest is internal rather than external – to overcome the demons of addiction to alcohol and painkillers. In 2004, he faced the loss of any opportunity to play baseball as a career, but he finally decided to set drugs aside and return to the game that he can play just about as well as anyone else on the planet. The closer he gets to completing his internal quest, the better he will be able to play. For those who follow his career with interest, watching him take up the mantle of hero, that he had almost cast aside while falling prey to his addiction, will either confirm his status as a hero, or mark him as a failure who threw opportunity after opportunity away. 3. Most of the ancient creation myths have several commonalities, and the same is true for the Hebrew, Greek and Seneca stories of the origin of the universe. For example, all three stories begin with an emptiness or void that is waiting to be filled. In the Seneca and Hebrew myths, all that is in the darkness is water; in the Greek and Seneca myths, birds are the only creatures that exist before the creation of the world. In the Hebrew myth, the spirit of God is hovering above the waters, instead of a bird. In the Seneca myth, there is a heaven already; in the Hebrew myth, the existence of a heaven is implied by the fact that God felt the necessity to talk in order to bring items into creation – the implication is that there is an audience to hear the things that God has to say. The purpose of creation is the most intriguing in the Seneca myth. The daughter of the mighty chieftain (the ruler of heaven) was sick, and the only way to cure her was to send her through a hole dug near a massive tree, sending her into the void below. The waterfowl below saw her falling and caught her, but wearied from her burden. Conspiring with the turtle and the toad, they created land, and the woman soon conceived a child. That woman’s daughter would give birth to the twins who would start life spreading on the planet, before one slew the other. This is similar to the Hebrew myth in a couple of ways. First, there would be a pair of brothers who would come into conflict, and one would kill the other. Also, the idea of conception without sexual union is part of the Hebrew story, but not until much later, when, according to the New Testament, Jesus was conceived without sexual union. The Greek myth also features a pair of brothers bringing forth people and animals onto the planet, as Prometheus made people and Epimetheus created the animals. Both the Hebrew and Greek myths contain the element of there being a transgression that would damage the fellowship between the gods and their creations. The decision of Adam and Eve to try the fruit from the tree of the knowledge of good and evil ended their stay in the Garden of Eden. Two similar events happened in the Greek creation myth, as Prometheus decided to give man the gift of fire, which led to Prometheus’ eternal punishment. Zeus also gave Pandora the gift of curiosity, as well as a box that she was never to open. When she opened the box, all of the evils in the world spilled out of it, leaving only Hope behind. Both myths explain how evil enter the world; in the Seneca myth, the possibility for evil is already present in the boys.

How Technology Has Changed Today s General Public...

Everybody utilizes innovation consistently whether it be our phones or a pacemaker in an elderly individual s body. The mechanical advances make regular daily existence simpler for everybody. Individuals would state that our era is dependent or ward of innovation. Some would rapidly make that supposition about our era. Individuals in my era could be delegated dependent on their telephones, computer games and so forth since they invest such a great amount of energy of those gadgets. Others would state it is an interest, for example, perusing a book or playing a round of golf. There are some in the more established eras are additionally extremely reliant on innovation, for instance one may have a clock on top of their solution to remind them to take the pills. It is not only the elderly that would be subject to innovation are the general population with handicaps are exceptionally reliant on innovation. Everybody has some kind of reliance on innovation in some sort of way. In today s general public innovation is all around, and new innovation is being created step by step. As I would like to think the general population who say that the more youthful era is subject to innovation isn t right. The more youthful era is simply utilizing the assets accessible to us, whether it be a PDA or a portable PC for school. The more established era is similarly as reliant on innovation whether it be prosthetic appendage or a computer game for PTSD patients. There is a myth sayingShow MoreRelatedHow Electricity And Telephone Changed The Way Of Communication907 Words   |  4 Pages In this essay Marvin, discusses how electricity and telephone changed the way of communication in America. 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We are absolutely living in the data age with innovation that is onlyRead MoreTechnology And Its Impact On Technology1468 Words   |  6 PagesThe technology has presented an enormous deviation in this world, and alike somewhat deviation, it has not been easy to become accustomed to the variations presented. In this report, it is possible to highlight that this technology consumes some time for some people to take its ideas. The main motive why this technology hasn’t been well expected is for the reason that its ethical issues that has been presented by the technological. In respect to how the technology has been received, it is seriousRead MoreThe Problem Of Computer Programming Education974 Words   |  4 Pagesthe Educate to Innovate Initiative, the current administration has made the case that investments in STEM programs (science, technology, engineering and math) are essential to maintaining the status of international competitiveness. 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The development of the computer has incredibly influenced expressions of the human experience, the business world, and society and history in numerous diverse zones, yet to see how extraordinary these progressions are, itRead MoreThe Impact Of Traditional Sms And Ott Messaging1286 Words   |  6 Pagescovered the SMS technology and the differences between traditional SMS and OTT messaging, this section is covering two theories on technological change; the technology S-curve and the dominant design in relation to the SMS technology. Technology S-curve A technology s-curve is a theory of innovation, it is based in the lifecycle of a technology, from the initial phase, where the technology is completely new, rather unused, to the point where a major breakthrough occurs and the technology is widely usedRead MoreBiography of Steve Jobs1830 Words   |  7 Pagesrequires carefully examining the company and his effect on the firm. This will be accomplished by providing: a description of the company, organizational performance determinants and the status of the firm today. Together, these different elements will highlight Jobs influence on Apple and how it was able to transform itself in a competitive marketplace. Description of the company (vision, mission, products / services and the industry etc.) Apple was founded in 1977. The vision and mission ofRead MoreHow Social Media Affects Tangible Relationships Social Interaction1698 Words   |  7 PagesFor the past couple of years, I’ve noticed a change in how my generation communicates with one another in comparison to older generations. Recent technological innovations, such as social media networking sites has been said to alter the way in which we communicate and the interaction we obtain face-to-face. According to Charles Blow, author of Friends, Neighbors, and Facebook, â€Å"Social networks are rewiring our relationships and our keyboard communities are affecting the attachments in our actual

What Would Life Be Like Without The Police Essay - 786 Words

History of the Police. What would life be like without the police? Would it be survival of the fittest, the law of nature? What actually happens when police are not present? In a small town in Oregon, budget cut-backs caused the entire police force to be laid off. When this happened, was there anarchy? No. The citizens organized their own volunteer police force to take the place of the police (Henry, 2014). This shows that if there is no police presence, one is necessarily created by the citizens who see the need for it. Even in nature, herds of horses have a lead stallion, lions have a dominant male and even birds follow one leader. This shows that society and nature need someone or something to lead them in the right direction. Society has come to see the police as the ones to go to when they are in trouble. Children are taught to look for a police officer if they need help, and the police are the first ones that most people call when they are in trouble. This must mean that they are seen as a necessity in today’s society. Whether they are seen as good or evil, they are vital. Although they are many times depicted as feeling entitled, they are governed by the same laws as everyone else, and they remain an essential part of the social order. When the British came to this country, they brought their cultural heritage with them, including their criminal justice system (South University Online, 2016). This included: 1. Limited police authority. 2. LocalShow MoreRelatedPolice Officers And The Law Enforcement970 Words   |  4 Pageslaw that need to be brought to justice. All police officers are trained to know all the rules and regulation in the handbook of the court of law. One the contraire, there are still some individuals police officers in this nation who abuse their authority of having those rights to serve and protect us from danger. 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The Impact Of Music On The Heart Of New Orleans Essay

Throughout history, the rise of cultures led to the rise of cultural difference, and those differences both separated people and brought people together. Every culture must choose whether they will allow their differences from other cultures to cause political unrest and war or collaboration and growth. While most people within almost every culture strives for peace, political and religious radicals cry out that deaths of infidels will lead to the greater good. But the sons and daughters of the mothers and fathers, and the brothers of the sisters and the husbands of the wives, taken from them fail to feel justified. Though, in spite of waves of cultural differences causing ebbs and flows of the rising and falling of war and peace, the cohesive effects of music seems to ignore cultural differences by giving them reasons to celebrate their commonalities. And the almost infinite number of sounds that creates the musical kaleidoscope of jazz perhaps best embodies music’s cohesive elements. Birthed in the heart of New Orleans in the late 19th and early 20th centuries, the cultural impact of jazz influences the world like few other musical genres. Its commonality acts like a musical palette painting pictures of versatility among those influenced by it. That commonality brought jazz musicians Russell Scott (more commonly known as Russ Liquid) and Andrew Block, who met while touring with European DJ and producer Grammatik, together. On that tour, Liquid and Block discoveredShow MoreRelatedEssay on Jazz Ken Burns1444 Words   |  6 Pagesfirst episodes entitled, â€Å"Gumbo, Beginnings to 1917† and â€Å"The Gift (1917-1924), explain the early growth of jazz as it originates in New Orleans and its expands to Chicago and New York during the Jazz Age. In assessing the first two episodes of Ken Burns 2001 documentary, JAZZ, this essay will explore the history of jazz, the musics racial implications, and its impact on society. In doing so, attention will al so be given to the structure of the documentary, and the effectiveness of documentary filmRead MoreJazz : The Emergence Of Jazz1301 Words   |  6 PagesJazz: the Emergence Jazz is a form of music that originated in the United States during the early 1900s. It was a form of music that influenced generations, started the music careers of many great musicians, and its ties are in the melting pot of New Orleans. Jazz has a variety of styles to listen to and also has many diverse musicians to experience these diverse sounds from. Jazz is a great form of music. Its different styles and musicians give it diversity, while strengthening ties to the AfricanRead MoreJazz Music1079 Words   |  5 Pagesjazz is New Orleans. Being a port city (with people migrating from all over the world), it was a melting pot of diverse racial composition. Atkins (1995, p18) observes that unlike the stern protestant ethic found elsewhere in the south, the attitudes prevailing in New Orleans encouraged dance and music. It was a city with a great culture of celebration and rich music tradition, a city with a nightlife that allowed musicians to play with, and learn from, one anothe r. These elements united in New OrleansRead MoreBridging Cultural Divides Through Music1281 Words   |  6 PagesBridging Cultural Divides through Music Throughout history, the rise of cultures caused the rise of cultural difference, and those differences separated and brought people together. Every person in every culture must choose to either allow their differences from people in other cultures to cause political unrest and war or collaboration and growth. While most people within almost every culture strives for peace, political and religious radicals cry out that the deaths of infidels will lead to theRead MoreBlues and Jazz Influence Paper1698 Words   |  7 Pages The Influence of 1920 Blues and Jazz on Modern Music Mark Carter The Influence of 1920 Blues and Jazz on Modern Music This paper is will try to show how the music that started with singing of old songs by the slaves to influences the music that the world listens to today. Shaping the music of Rock and Roll, Country and Western, and Easy Listening that influences every aspect of society’s everyday life are Blues and Jazz. In an interview many years ago on television, heard by thisRead MoreThe Culture Of Folk Culture863 Words   |  4 Pagespractice, their habitual meals, or their ancient legends that they speak about to future generations. Any culture must have a birth place, most of them spawning in local communities, or erupting from a specific group of individuals. 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Born on August 4, 1901, in the poorest section of New Orleans, Armstrong grew up with his grandparents due to his parents’ separation. On January 1, 1913 he made a mistake which turned out to be the best thing that ever happened to him. At a New Year’s celebration in downtown New Orleans, Louis Armstrong, also known as â€Å"Satchmo† and â€Å"Satch†, fired a pistol into the air and was placed in the Colored Waifs’ Home. It wasRead MoreThe Jazz Musici Louis Armstrong1078 Words   |  5 Pages â€Å"Jazz music came to seem not merely an annoyance but a threat,† (â€Å"Roaring Twenties†). The 1920’s was when Jazz became a hit: it was being played in roadho uses, dance halls and places all over the country. It’s a very fast paced sound that was different than what people had heard of before. Louis Armstrong was a very well-known jazz musician during this time. He played the trumpet and was a very good jazz musician. He was in many bands, played in Broadway and traveled playing jazz for people (Wenning)Read MoreThe City Of New Orleans1805 Words   |  8 Pagesâ€Å"New Orleans is unlike any city in America. Its cultural diversity is woven into the food, the music, the architecture – even the local superstisions. It’s a sensory experience on all levels and there’s a story lukring around every corner.† –Ruta Sepetys The city of New Orleans, Louisiana is a very different and special place. The population is about 390,000 based on the 2014 census. The city was named after the Regent to Louis XV, the Duke of Orleans in the early 1700s. It was established by

INFORMATION SYSTEMS DEVELOPMENT PROCESS Essay Example For Students

INFORMATION SYSTEMS DEVELOPMENT PROCESS Essay Oliver LinesBABS 2 OptionManaging Information Systems In OrganisationsRECENT ADVANCES IN SYSTEMS DEVELOPMENT WILL RADICALLYCHANGE THE INFORMATION SYSTEMS DEVELOPMENT PROCESSINTRODUCTIONIn recent years, there has been an abundance of new technologies in the information systems field. These new technologies have altered the very development process itself. Information systems have gone from being a series of one level databases to three dimensional reality, virtual reality, and multimedia systems. In the early days of information systems, the demands were for data, with no real function of artificial intelligence. However, as the 21st century approaches, business has taken on an entirely different function, and the need for individual information systems has grown immensely. This demand for information technology is in all areas of business: corporations, law, medicine, science and even small business. In addition, the worldwide web and the Internet have added an additional factor of c ommunications. Most information systems in use today require at the very least, a measure of Internet capability. In order to understand the changes in these development processes, the history of databases should be analyzed. BACKGROUNDDatabase Management Systems actually began in the 1950s, with what is known as the first generation, also known as file systems on tape. The major task of any computer in those days was to process data under the control of a program. This primarily meant calculating, counting and simple tasks. Second generation databases, file systems on disk, allowed use of computers in dialogue mode as well as batch mode. The development of magnetic disks allowed for more sophisticated file systems, making multiple access possible. These first two generations of DBMS were characterized by the availability of file systems only; strictly speaking these were the forerunners of database systems, the foundations. An important component of these database systems were the static association of certain data sets (files) with individual programs that would concentrate on these. There were high redundancy problems between files; inconsistencies when one program made changes that are not made in all programs; inflexibility against changes in applications; low productivity by programmers since program maintenance was expensive; and the problem of adopting and maintaining standards for coding and data formats. The third generation, pre-relational databases, started in the 1960s and continued into the 1970s. This generation is characterized by the introduction of a distinction between logical and physical information, along with a parallel need to manage large collections of data. Data models were used for the first time to describe physical structures from a logical point of view. With this distinction between the logical and physical information, value systems were developed which could integrate all the data of a given application into one collection. The fourth generation consisted of relational databases and began in the 1980s, resulting in database systems that could store data redundancy free under a central control and in a clear distinction between physical and a logical data model. Systems based on relationship modeling occurred during this period of time. The systems based on relationship modeling are provided with a high degree of physical data independence and the availability of powerful languages. Less of the system is visible to the user, with changes taking place in the background. A shift from record orientation to set orientation marks this fourth generation. As of 1991, there was a fifth generation predicted, post-relational, which we are currently experiencing, and perhaps surpassing. Other applications can benefit from database technology. The development of extensible systems, logic-oriented systems, and object-oriented systems are part of this generation. R.G. Cattel speaks of the changes seen in the last fifteen years:The past decade has seen major changes in the computing industry. There has been a widespread move from centralized computing to networked workstations on every desk. We have seen an entirely new generation of software aimed at exploiting workstation technology, particularly in engineering, scientific and office applications. In database systems, there have been major changes in products for business applications, including the widespread acceptance of relational DBMSs. However, existing commercial DBMSs, both small-scale and large-scale, have proven inadequate for applications such as computer-aided design, software engineering, and office automation; new research and development in database systems has been necessary. (Cattell 1991)The very nature of these new object oriented databases has caused changes right down to the programming level. As we near the end of this century, designers are now looking at databases that can predict the side effects of medicines, eliminating the need for human trial subjects. Other programs are being designed to put in data for architecture to check building integrity. Car manufacturers are able to input data and have three-dimensional models to experiment with, regarding stress factors and damage. With so much new technology erupting every day, some needs have developed for a standardization of protocols and a way to store all the data. DEVELOPMENTSMark Hammond (PC Week) talks about a new development for standardization. IBM has developed DRDA (Distributed Relational Database Architecture) which is a standard interoperability protocol for databases and applications. The DRDA was developed in 1989, and is finally out into the public domain and ready for use. Data warehousing is a new development on the Information System front, and is actually the culmination of new developments in data technology. Gabrielle Gagnon identifies these developments. They include entity-relationship modeling, heuristic searches, mass data storage, neural networks, multiprocessing, and natural-language interfaces. She goes on to say the data warehouse is a centralized integrated repository of information, one that can provide a vital competitive edge for product development. There are several types of data warehouses, including the operational data store (ODS), the data mart, which is of value in analyzing sales, and the enterprise warehouse, which can do both a centralized and distributed approach. The Young Years: Adoph Hitler Was Born On April 20, 1889 And Lived In EssayTHE PRESENTCost in technology is becoming less of an issue. Prices have come down significantly in the last few years. Demands on space and technology in a PC have been lowered, and concurrently, computer technology has experienced huge growth, resulting in advanced technology installed on computers at the sale point. Our friends in America spent $200 million on voice recognition software in 1997, and by the year 2001, that amount is expected to jump to $3 billion, according to the consulting firm Voice Information AssociatesThe systems development that is taking place at the end of the 20th century is vastly different than what took place in the early days of computers. Technology today requires communication between the designers of systems and their clients. Businesses want individualized systems, but these systems need to be able to cross international lines, requiring uniform standards across the board. Customer information systems are being used in industries today. These systems allow companies to understand individual consumers as well as identify new markets and develop new and innovative technology products. Companies are able to take advantage of the following factors:1. Databases are far larger today than they were even in the fairly recent past. These databases may include information on tens of millions of households. 2. The depth of information generated from databases on individuals and households can be much greater. 3. This information can be used as part of a highly automated business function. The development of information systems needs to keep pace with these changing demands from industry. In the past, programs were designed as large, umbrella programs, meant to serve the same clientele. Today, however, there is more individualization required for information systems. This also results in the need for flexibly trained programmers and designers; they must be able to custom design systems where necessary. Even small businesses like RWH Enterprises LTD (a small import/export company in Cambridgeshire) are not content with settling for systems designed to cope with their broad sector. They require a system that will cater for all their business needs and are now employing programmers, who together with management look for any ways the business process can be simplified and made more efficient with the use of individualized systems. CONCLUSIONThere has been an ongoing evolution in the information systems area. Initially there were electronic processing systems; these were replaced by management information systems. In the 1980s, decision support systems emerged to facilitate the decision making process. These programs provide flexibility and insight into changing managerial needs. In the last decade, emphasis was placed on strategic needs, and strategic information systems emerged. One common thread throughout the evolution of these systems is the increasing need for programs that can think. That provides a challenge to systems programmers and changes the way systems are developed. Another factor affecting the development of information systems is the growing need for systems in the international market. Systems that are developed need to be standardized and a set of protocols need to be in place to ensure equal development across the board. Information systems are an area that would benefit from ISO 9000 standards; these standards provide uniform code worldwide. As the twenty-first century approaches, the development of information systems is going to continue to change. It has evolved from a strict database to multi-function abilities in just twenty years. In the last decade virtual reality, three dimensional imaging, artificial intelligence and reliance on the Internet have emerged. Developments will have to occur quickly, no longer having the luxury of being able to take years to develop. Systems will need to emerge that help and assist the systems development process. After all the world is now affectivity smaller; it now takes only minutes for something to electronically travel to the other side of the globe and this means Information Systems development will have to keep pace, staying slightly ahead of the rapid changes continuously taking place. We are now in the Information Revolution and have become fairly reliant on technology. It is a fact that 90% of the scientists that our planet has ever seen are alive today and therefore we are going to see rapid development in this area, not only in technology but also in functionality. Information Systems will rapidly emerge into a wealth of applications until it eventually becomes an extension of the person (maybe in a literal sense some day). That, I believe, is the future. BIBLIOGRAPHYCattell, R.G.G., Object Data Management: Object-oriented and Extended Relational Database Systems Reading, MA: Addison-Wesley Publishing Company, 1991. Gagnon, Gabrielle, Data Warehousing: An Overview PC Magazine, March 9, 1999. Hammond, Mark, DRDA Standard Could Finally Get Rival Databases Talking, PC Week, December 7, 1998. Mayor, Tracy, Look Ma, No Hands PC Week, July 1, 1996. Coskun, Samli A., Information-Driven Marketing Decisions: Development of Strategic Information Systems Quorum Books, 1996. Schindler, Esther, The Computer Speech Book Academic Press, Inc., 1996. Sichel, Daniel E., The Computer Revolution An Economic Perspective The Brookings Institution, 1997. Vossen, Gottfried, Data Models, Database languages and Database Management Systems Wokingham, 1991. Watterson, Karen, Blueprint For A Database Data Based Advisor, June, 1990. Category: Technology

Automatic Sentence Generator free essay sample

Not only will we be able to automatically generate sentences associated with the theme being modeled, but we will also be able to help recognize phrases and sentences. In other words, this is a module which could be part of an automatic speech recognition system, so that proposed recognized word sequences can be validated according to acceptable contexts. The system is adaptive and incremental, since models can be modified with additional training sentences, which would expand a previously established capacity. Key words: corpus, vocabulary, training, recognition, recognizer, generator, histories, context, decoder. . Introduction. The growing, unstoppable development of very high speed information processing computers with tremendous main memory capacity which we see today leads us to think that it will be possible to design and construct automatic speech recognition systems which can detect and code all the grammatical components of a training corpus. As part of our effort to make a contribution to the fascinating world of Automatic Speech Recognition, we have developed a system composed of a set of computer programs. We have observed that on the basis of a model of a small corpus made up of sentences in a particular context, we can automatically generate a great quantity of grammatically correct sentences with this context. Also, our system can effect a linguistic discrimination to the point of rejecting, as out of context or grammatically incorrect, those word sequences with words or word histories not registered in its memory. We believe that a system that processes information in the way we describe in this paper can work successfully in recognition tasks of a variety of context whose vocabulary size extends to thousands of words. . Terminology. Training corpus. The set of sentences and paragraphs used to construct the context model used for the generation and recognition of phrases. Vocabulary. The set of distinct words found in the training corpus. Training. Process which results in the creation of context models. Histories. Sets of words that appear contiguously in the training corpus, [1] . For example, if the following sentence is part of the training corpus â€Å"there are three reasons which seem to be the origin of this fact†, then a history of two words could be â€Å"the origin†, and a history of three words would be â€Å"the origin of†. Context. Knowledge area to which belong the sentences and paragraphs of the training corpus. Recognition. The processing of a word sequence and deciding whether it is a valid sentence with regards to the grammatical rules which have been established in the context model. Sentence generation. The process which creates a sentence based on the context model. Grammatically valid sentence. A sentence which has a structure which follows the grammatical rules that have been detected in the training corpus. Sentence hypothesis. The set of possible sentences corresponding to a word sequence that is to be recognized or generated. 3. Context model generator. In figure 1, we show the principal elements of the system, the inputs, the outputs, and a graphical indication of how the elements interact. A word sequence Linguistic decoder Recognized sentence Training corpus Context model Vocabulary Generator of sentences Genered sentence Figure 1. System structure. To create a model, we begin with a grammatically correct set of sentences and paragraphs pertaining to the context which we wish to model. We model the context, even though we believe that if we feed the model incrementally, we could eventually obtain a good model of the language to which the training corpus belongs. The training or modeling of the context consists basically of the search, coding and saving of the occurrence of contiguous word histories corresponding to the sentences and paragraphs of the training corpus. We create coded blocks of word histories. The words in the histories are coded by way of whole numbers. In figure 2, we give an idea of how we code the histories in the training corpus. The first word that appears in the first training corpus (recall that we can work incrementally with various corpus) is assigned the number 1, then, the next different word is assigned the number 2, and so on. The nth different word is assigned the number n. text to whole numbers histories of two words the origin . . . . . . . . the origin of . . . . . . . 9 10 .. .. .. .. .. .. .. .. 9 10 11 .. .. .. .. .. .. .. .. text to whole numbers histories of three words Figure 2: Examples of histories blocks taken from the training corpus. The following blocks are formed: A block which has a coded list of the words which begin sentences in the corpus; this block is used by the generator of sentences and is called Block 1. A block which has a list of histories of 2 words. This block may be used by the generator of sentences as well as by the linguistic decoder. We call this Block 2. A block which has a list of histories of 3 words; like Block 2, it may be used by the generator of sentences as well as by the linguistic decoder. This block contains triplets of words in the training corpus. We will call it Block 3. A block which has a list of histories of 3 words like Block 3, but with the difference that these histories only include the ends of sentences and paragraphs of the corpus. This will allow the sentence generator to know how to finish sentences. This is Block 4. The coding of the training corpus in Blocks 1, 2, 3 and 4, facilitates the creation of sentence hypotheses for linguistic recognition, for sentence generation, and to make data processing more agile. Once these blocks have been created and coded, some histories are discarded to reduce some redundancy in the coding, and to accelerate the programs that recognize sentences and generate sentences. History elimination is somewhat arbitrary; for example, some experiments were performed, on the one hand, eliminating the histories of two words of low frequency, and on the other, saving the histories of 3 words which began with these two words. Suppose the combination â€Å"subject to† appears only once, and the sequence â€Å"subject to conditions† also appears; then the 2 word combination would be eliminated. Whereas if the combination â€Å"the history† appears seven times, then it would be saved. Working in this fashion, we hope to assure that even those histories that have low frequency will still be accounted for when we do recognition and generation. It is clear that if we exclude many histories of 3 words, overall system performance will degrade. Another option is to add more sentences to the corpus with the sequences that are being eliminated, which would require additional training. This is the one we prefer, since the model which is created in this fashion is more complete, adaptive and incremental, and allows us to repeat the procedure with another training set, without losing information obtained during the previous passes. So that in an incremental fashion, we can enrich not only the model vocabulary, but also increase its capacity to generate sentences and perform recognition. The process of retraining or adaptation is done after we have created a model based on a corpus which we will call Corpus 1. We can select other sentences of the same context, different from the original, and thus make Corpus 2. Using this new corpus, we code all its histories, and add the frequency of appearance to those that appeared in Corpus 1, thus creating an accumulated frequency for these histories. We see that we have thus created an extension to the model created with Corpus 1, to which we are adding new histories and even new words to its vocabulary. This process can be repeated as many times as considered necessary for our applications. Nevertheless, finally, we only store in Blocks 1, 2, 3 and 4 the histories of two words that have the most frequency, plus the histories of three words which initially contain those two word sequences that have been eliminated. 4. Sentence generator. Based on the model described previously, the system is capable of randomly producing sentences with the given context. The algorithm for generating sentences is as follows: 1. A word from the Block 1 list is randomly selected, say w1, and is shown on the system monitor. For example, the word â€Å"This† is selected from Block 1. 2. From Block 3, those histories which begin with the word w1 are grouped into a new block, the subblock 31. To continue the previous example, we would then have â€Å"This specification should†, â€Å"This declarative sentence†, â€Å"This sentence belongs†, â€Å"This distinction allows†, â€Å"This plays a†, etc. 3. If the subblock 31 is not empty, then one of the alternatives is randomly selected, and the next two words which follow w1 are shown on the system monitor. For example if the history â€Å"This sentence belongs† was selected, then â€Å"sentence belongs† appears on the screen. 4. We then proceed with a search for histories in Block 2. The ones of interest in this block are those who have as a first word the last word which appears in the history selected from subblock 31, when this is not empty, say histories that begin with w3. If subblock 31 is empty, then the histories of interest are those that have as an initial word w1. In this way, a new subblock, subblock 21, is formed. Continuing our example, we might have only â€Å"belongs to†, and subblock 21 would have only one entry. 5. If subblock 21 is not empty, one of its entries is selected randomly, and the second word of this history is shown. Following the previous example, the word â€Å"to† appears. So far we have the phrase â€Å"This sentence belongs to†. 6. The process continues with a search in Block 4 which contains sentence ending histories. The ones of interest would be those that begin with the last word in the last history previously selected. We then form subblock 41. For our example, the histories of interest would be those that begin with â€Å"to†. If no histories are found that begin with this word, then subblock 41 would be empty. 7. If subblock 41 is not empty, then one of its entries is randomly selected, and the last two words of the sentence are shown. In this way, a sentence generation has been completed. . If subblock 41 is empty, then w1 takes the code associated with the last word. In this example, the coded associated with to. 9. Go to step 2. In our example, after going to step 2 and when the process is completed, we got the sentence this sentence belongs to a syllabic rhythm. The majority of the sentences that are produced in this fashion are not to be found in the training corpus, but rather have been formed from an appropriate linking of coded histories. The sentence that we formed in our example does not exist in our corpus, but others do that we indicate as follows : a. â€Å"This type of sentence belongs to a neutral annunciation without expressive aspects and specials appellative†. b. â€Å"It belongs to the last syllable bearing of lexical accent in the melodic group†. c. â€Å"This declarative sentence is formed for three tonal units†. In summary, the sentence is generated through a successive search of words in the histories of Blocks 3, 2 and 4. The process ends when at least one history from Block 4 is found or when no history exists in any of these 3 blocks to continue the sequence. This may be the case when a history has been selected which in the corpus is at the end of a sentence, and is not ppropriate for forming a continuing sequence. For this work, the sentence generator descr ibed was designed and implemented as a computer program, in order to have an idea of the sequence of words that could be recognized by the linguistic decoder, which we would develop later on and which we will describe in the next section. At this moment, we can suppose that the context model is a network of coded histories that contain the sentences that can be recognized by the linguistic decoder. The utility of the sentence generator in this work was foreseen only to show the sentences present in the model and that therefore can be recognized. . Linguistic recognizer or decoder. The part of a speech recognizer that converts acoustic data from a pronunciation to a sequence of linguistic symbols (as for example, a sequence of phonemes, a sequence of words, etc. ) is called an Acoustic Decoder, while the Linguistic Decoder is that part of the speech recognizer which determines if that sequence of symbols corresponds to a valid sentence in the language. [2] As can be surmised from Fi gure 1, in this work we only develop a linguistic decoder so that any tests suppose the existence of a sequence of words as generated by an acoustic decoder. The procedure by which the system can recognize a sequence of words (w1, w2, , wn) [2] as grammatically correct based on a context model, is described as follows: 1. Receive the first word of the sequence, w1. 2. Determine if w1 is part of the vocabulary. If w1 is not part of the vocabulary, then it is rejected, as is also the sequence, since it is out of context. If it is part of the vocabulary, then it is shown on the system monitor. 3. Look for histories that begin with w1 in Block 2 and Block 3. In this way, we generate 2 new blocks of possible parts of sentences which, depending on the language being modeled, could be formed starting from w1. One of these blocks is a consequence of the search in Block 2, and will be called Block 21, and the other, a consequence of the search in Block 3, called Block 31. In this way, we generate partial hypotheses of sentences. We believe this constitutes a way of speeding up the process, since the search for the next word in the sequence is limited to Blocks 21 and 31. It is possible that there are no histories that begin with w1, that is, it is ossible that Block 21 and Block 31 are empty. In this case, in the model there are no words that can follow w1, and the recognizer will not admit the sentence and will finish the task of recognition. 4. We receive the next word of the sequence, w2. If it is part of the vocabulary, we search for its occurrence in the entries of Block 21 and Block 31; if not, we reject the sequence. 5. We discard from Blocks 21 and 31 those histories that do not have w2 after w1. If we still have entries that contain w2 following w1, then we show w2 on the screen. If not, we reject the sequence, and the recognition task is terminated. . We return to step 3, working now with w2 instead of with w1. In other words, each time that the decoder arrives at this point, one repeat a sequence of steps, working now with the last word recognized in the process. The recognition of a word sequence can end in two ways: a rejection when the decoder determines that the model is not valid or the sequence is out of context, or else, when the symbol $ is received which is the indicator of end of sentence, in which case we have a grammatically correct sentence. There can be a case in which sequences are rejected that are in context and are grammatically correct. This can be resolved retraining the model with new corpus of the same context. We can observe that the recognizer checks the word sequence received for correctness from the point of view of the grammatical rules associated with the context, and at the same time, if it is part of the context being modeled. Although the context models described can be thought to be a combination of bigrams and trigrams, in this work, we cannot talk about n-gram stochastic models, nor of finite state stochastic automatas [2], since the way in which decoding is done does not use probabilities as they do. In fact, this decoder does not measure the probability that the sequences are being modeled or not, but rather simply determines if it can form a sentence which is in the model, and if not, rejects it. 6. Tests. The following tests were performed: 1. An initial test was performed with a corpus consisting of 160 sentences and paragraphs of different lengths. We worked with lengths that varied between 3 and 63 words. The sentences were taken from a Spanish text about linguistics. The corpus had a total of 2563 words. . The vocabulary that would be handled by the recognizer module and by the generator module was determined. The vocabulary started out with 816 different words. 3. A search was performed on the text, to find Block 1, Block 2, Block 3 and Block 4, which form the context model. This process lasted approximately two hours to execute on a PC Pentium 133 Mhz. 4. Sentence blocks were generated. This process was repeated 30 times. Each block generated consisted of 10 sentences. 5. Sentence recognition was performed. To perform the test, word sequences were given, and then we had to determine if such sequences could re recognized using the context model 6. Some small corpus of 10 to 20 sentences was taken anew, and the process was repeated. 7. Results. 1. Additional corpus can be incorporated incrementally without losing the information codified in previous training. 2. The generated sentences are generally shorter than the ones used in training. 3. The number of sentences generated depends on the size of the training corpus. 4. The number of sentences generated which are valid with regards to grammar and context is 70% of the total that were generated in the tests. 5. The number of recognized sentences that are grammatically correct and that correspond to the context is close to 90% when they are selected so as to be similar to the ones in the corpus. 6. About 90% of the generated sentences are not present in the training corpus, with the exception of some short sentences, made up of 2, 3, 4 and up to 5 words. 7. It is not possible to recognize all the sentences and paragraphs, as they appear in the training corpus. . Conclusions. In incremental fashion, it is possible to obtain ever greater robustness in the recognizer module as in the sentence generator module. This modification of the model is obtained at the price of speed during the adjustment, which will depend on the application and the machine. In the same way that a large number of sentences can be generated, also a large number of sentences can b e recognized. Due to the large quantity of sentences that can be generated, it is also possible to recognize a large uantity of sentences and phrases that do not necessarily pertain to the context being modeled, but do pertain to the same language of the corpus. It is not possible to recognize all the sentences and paragraphs, as they appear in the training corpus, since not all the histories present in the corpus are coded in the system memory. This defect could be corrected by saving all the histories that appear in the text, but this would lead to slower searches when performing recognition or generation tasks. We could say we are doing language modeling, since we believe that the modeling of contexts in this fashion can be extrapolated to the language associated with the contexts. This type of linguistic decoder could work in recognition applications where the size of the vocabulary is on the order of thousands.