Speech Recognition Fundamentals Current and Future

Speech Recognition Fundamentals Current and Future

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Speech Recognition Fundamentals Current and Future

Human centric computing branches out into many areas. One of the most important one is speech recognition since this is the primary way for human beings to communicate. Therefore, it is only natural to use speech as the primary method to input information into a computational device or object needing manual input. The goal of human centric computing is to make technology as user friendly as possible and to integrate it completely into human life by adapting to humans’ specifications. Currently, computers force humans to adapt to computers, which is the opposite intend of human centric computing. Speech recognition has the basic intention to help humans easily communicate with computers and reap the maximum benefit from them. Though, speech recognition faces several problems in its implementation it is partially available in today’s life. The complexity of speech in not as obvious to humans because of their innate sense of grammar, which is not inherent to computers. Still, speech recognition is being used in daily life. Though, in the future speech recognition could be included in the simplest of items. When this is achieved complete human centric view is achieved regarding speech recognition.

The fundamental of speech recognition is to take sound and to translate this information into text and commands. This process is very complex since sound has to be matched up with stored sound bites and further analysis has to be done on sound bites, which have no clear match with preexisting sound pieces. To determine an undetermined piece of sound requires computing power to use grammatical rules and written rules using established models how often this word is used and how it relates to the other defined sound bites. The problem with speech recognition programs for text editors is the complexity of human speech. Therefore, “all text is constantly check to make sure that no grammatical or spelling errors end up in the final product”(HMW 17). This comes normal to humans who have an innate sense of sentence structure and word groupings. The recognition program has to recognize the input and form the sentence. This sentence will be base on a huge set of parameters, grammar, which defines human speech.

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To recognize an undefined or garbled sound and use the correct word output several combinations and iterations have to be done to get the correct word to finish the complete sentence. Though, “some systems can recognize only voices for which patterns have been programmed into the computer” (VRP 66). The problem is that speech recognition programs have to learn over time and adapt to the use of an individual. Though, some speech recognition programs “notably one being developed at Carnegie Mellon Univeristy in Pittsburgh, are versatile enough to accept any speaker’s voice as input” (VRP 67). Therefore, speech recognition program will come with a well-established set of grammatical rules but will need to learn in the process to become an even more sophisticated program. This will be done through continuos and discrete analysis by the speech program of the user’s triads and habits. Furthermore, updates of speech recognition programs to expand its parameters to establish well formed sentences and to extend its library of sound bites stored, which will be matched against the user’s input. These are some of the challenges facing Speech Recognition programs. The fundamentals of translating sound into text seem simple though the underlying computational tasks are extensive.

Speech recognition is steadily developing and perfected to achieve a final version, which will achieve the goal of human centric computing. Currently, speech recognition is in primitive stages compared to human speech in daily life. Speech recognition programs are able to record approximately 130 words a minutes while we use about 180 words in daily speech. Speech Recognition has not reached the ability to record 180 words a minutes but on the other hand it is still faster then any human being can type on a keyboard. This is only on small problem speech recognition is facing. For instance, speech recognition is not able to complete complex task like inserting a table or to save a document. “Speech input and synthesized speech output are convenient, allowing relatively hands-free control of a procedure and justifications for the development of voice controllers for applications…” this is a major idea behind human centric computing (VRP 47). But, for these task a keyboard still has to be used. Speech recognition is still in its infancy to recognize voice commands. Furthermore, speech recognition programs have a certain percentage of error due to nuances and sound similarities in every day language, which have to be manually corrected. Current speech recognition is used in simple task. For example, to call a stored number in a cell-phone. On the other hand there is no voice input to store a number. Speech recognition has to evolve to encompass all possibilities and complexities of everyday speech to be completely integrated into human life and to achieve human centric goal.

The future of speech recognition will encompass every imaginable object, which will need manual input. The goal of human centric computing is to adapt the computer to human needs. The usual input device, the keyboard, limits the human ability to use computers. With voice recognition and voice commands humans will be able to complete tasks faster. The problem is the communication between humans and computer since the input of data is the weakest. Voice commands will speed up this process since humans are able to communicate faster through speech then to use hands to communicate the same information. Complete speech recognition will add to the convenience of life and full fill the goal of human centric computing. This human centric computing will carry over into the everyday life. The developments done today will be available in a not too distend future on your computer and later trickle down to every day. Even today, we see that certain programs are already designed for computers. For instances, Carnegie Mellon University designed Sphinx 2, which “is a decent candidate for handheld, portable, and embedded devices, and telephone and desktop systems that require short response times”. You will no longer have to set the time on a microwave. In the future the time will be set with a voice command interpreted by speech recognition. We will no longer be accustomed to the task of manually inputting values or pieces of data into a device since these tasks will be replaced by voice commands and speech recognition.

Speech recognition is an important part of human centric computing. The goal of human centric computing is to adapt technology to human specifications. Currently, computers force humans to adapt to computers, which is the opposite intend of human centric computing. Speech recognition has the basic intention to help humans easily communicate with computers and reap the maximum benefit from them. This includes simple interactions with objects that contain computational power like toys and cell-phones to high-end computers. The purpose of speech recognition is to make it easier to use these devices and even to achieve unison with these items. This is achieved through unintentional use due to the fact of these computational items have been completely integrated into human life through speech recognition.

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Holsinger, Erik (1994), How Multimedia Works.
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Lenzo, Kevin. (2000). “CMU Sphinx”. Carnegie Mellon University. http://www.speech.cs.cmu.edu/

Navratil, Jivri. (2001). “Spoken language recognition--a step toward multilinguality in speech processing.” IEEE Transactions on Speech and Audio Processing, 678-85.
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