Speech Recognition Principles And Applications Essays - Linguistics

Speech Recognition: Principles And Applications Table of contents Abstract 3 Overview of the Characteristics of Automatic Speech Recognition Systems 4 Number of Words 4 Use of Grammar 5 Continuous vs. Discrete Speech 5 Speaker Dependency 6 Early Approaches to Automatic Speech Recognition 6 Acoustic-Phonetic Approach 7 Statistical Pattern Recognition Approach 8 Modern Approach to Automatic Speech Recognition 8 Hidden Markov Models 9 Training of an Automatic Speech Recognition System Based on HMMs 11 Sub-Word Units 11 Applications of Automatic Speech Recognition Systems 12 Automated Call-Type Recognition 13 Data Entry 13 Future Applications Using Automatic Speech Recognition Systems 14 Conclusion 14 References 15 Abstract With the advances of technology, a lot of people may think that integrating the ability of understanding human speech in a computer system is a piece of cake. However, scientists disagree. Since the early nineteen fifties, scientists have tried to implement the perfect automatic speech recognition system, but they failed. They were successful in making the computer recognise a large number of words, but till now, a computer that understands everything without meeting any conditions does not exist. Due to the enormous applications, a lot of money and time is spent in improving speech recognition systems. SPEECH RECOGNITION: PRINCIPLES AND APPLICATIONS Nowadays, computer systems play a major role in our lives. They are used everywhere beginning with homes, offices, restaurants, gas stations, and so on. Nonetheless, for some, computers still represent the machine they will never know how to use. Communicating with a computer is done using a keyboard or a mouse, devices many people are not comfortable using. Speech recognition solves this problem and destroys the boundaries between humans and computers. Using a computer will be as easy as talking with your friend. Unfortunately, scientists have discovered that implementing a perfect speech recognition system is no easy task. This report will present the principles and the major approaches to speech recognition systems along with some of their applications. Overview of the Characteristics of Automatic Speech Recognition Systems How can we evaluate a speech recognition system? Obviously describing it by good or bad isnt enough since the performance of such a system may be outstanding in one application and poor in another. In fact, speech recognition systems are designed according to the application. Some of these variable characteristics are presented below. Number of Words The major characteristic of a speech recognition system is the number of words it can recognise. The question that comes to mind is how many words are enough so that the performance of a speech recognition system is acceptable. The answer depends on the application (6, p98). Some applications may require few words, like automated call-type recognition, others may require thousands, like data entry. However, increasing the number of words or the vocabulary of a speech recognition system increases its complexity and decreases its performance (probability of error is higher)(6, p.98). Systems with large vocabularies are also slower since more time is needed to search a word in a large vocabulary. Increasing the number of words isnt enough because the speech recognition system is unable to differentiate words like to and two or right and write (6 ,p.98). Use of Grammar Using grammar, differentiating words like to and two or right and write is possible. Grammar is also used to speed up a speech recognition system by narrowing the range of the search (6,p.98). Grammar also increases the performance of a speech recognition system by eliminating inappropriate word sequencing. However, grammar doesnt allow random dictation which is a problem for some applications (6, p.98). Continuous vs. Discrete Speech When speaking to each other, we dont pause between words. In other words, we use continuous speech. However, for speech recognition systems, there is difficulty in dealing with continuous speech (6, p.98). The easy way out will be using discrete speech where we pause between words (6, p.100). With discrete speech input, the silent gap between words is used to determine the boundary of the word, whereas in continuous speech, the speech recognition system must separate words using an algorithm which is not a hundred per cent accurate. Still, for a small vocabulary and using grammar, continuous speech recognition systems are available. They are reliable and do not require great computational power (6, p.100). However, for large vocabulary, continuous speech recognition systems are very difficult to achieve, require