Speech recognition system seminar
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Speech recognition systems convert spoken words to text in real-time. They are used in dictation software and intelligent assistants. Design challenges include background noise, accent variations, and speed of speech. Speaker dependent systems recognize one voice, while speaker independent systems recognize any voice without training. Speech is broken into phonemes and a hidden Markov model identifies phonemes and language models recognize words. Components include signal analysis, acoustic and language models. Applications include healthcare, military, phones, and personal computers. Siri and Google Now are examples of intelligent assistants using these techniques.
Speech recognition system seminar
- 1. SPEECH RECOGNITION SYSTEMS TWINKLE SAHU CSE 6TH SEM
- 2. INTRODUCTION • Speech recognition is a process by which a computer takes a speech signal (recorded using a microphone) and converts it into words in real-time. It is achieved by following certain steps and the software responsible for it is known as a ‘Speech Recognition System’ • SR systems are usually implemented in the form of dictation software and intelligent assistants in personal computers, smartphones, web browsers and many other devices.
- 3. DESIGN OF A SR SYSTEM SR systems have to deal with a large number of challenges like :• The speaker’s voice is often accompanied by surrounding noise which makes their accurate recognition difficult. • A speaker may speak a number of different words and all of these words have to be accurately recognized. • Accent of speaking varies from person to person and this is a very big challenge • A speaker may speak something very quickly and all of the words spoken have to be individually recognized accurately.
- 4. TYPES OF SR SYSTEMS • Speaker Dependent SR systems : Work by learning the unique characteristics of a single person’s voice and depend on the speaker for training. • Speaker Independent SR systems : Designed to recognize anyone’s voice, so no training is involved.
- 5. BASIC PRINCIPLES OF SPEECH RECOGNITION • The smallest unit of spoken language is known as a Phoneme. • The English language contains approximately 44 phonemes representing all the vowels and consonants that we use for speech. • We can take the example of a typical word such as moon which can be broken down into three phonemes: m, ue, n.
- 6. • To interpret speech we must have a way of identifying the components of spoken words and phonemes act as identifying markers within speech. • An algorithm has to be used to interpret the speech further. The Hidden Markov Model is a commonly used mathematical model used to do this. • To create a speech recognition engine, a large database of models is created to match each phoneme. • When a comparison is performed, the most likely match is determined between the spoken phoneme and the stored one, and further computations are performed.
- 7. COMPONENTS OF SPEECH RECOGNITION • Corpus Collection : Database consisting of speech data that built from multiple speech samples.
- 8. • Corpus collection construction for a speakerdependent SR system :-
- 9. • Corpus collection construction for a speakerindependent SR system.
- 10. • Signal Analyzer : Analyses the speech signal and removes the background noise thus focusing only on the speaker’s speech . • Acoustic Model : Identifies phonemes from the speech sample using a probability based mathematical model. ACOUSTIC MODEL
- 11. • Language Model : Identifies words and thus sentences uttered by the speaker from the phonemes by making use of a dictionary file and grammar file. DICTIONARY FILE GRAMMAR FILE
- 15. LANGUAGE MODEL /p/--/ae/--/n/ DICTIONARY FILE pain pain GRAMMAR FILE pain TEXT OUTPUT
- 16. The Grammar File
- 17. HIDDEN MARKOV MODEL • Markov models are excellent ways of abstracting simple concepts into a relatively easily computable form. • Used in data compression to sound recognition. From this graph we can create sequences such as: N1 N2 N3 N1 N2 N2 N2 N3 N3 N3 N3 N3 N1 N1 N2 N2 N3
- 18. N1 N2 N3 = 0.4 X 0.8 X 0.5 = 0.16 N1 N2 N2 N2 N3 N3 N3 N3 N3 = 0.4 x 0.2 x 0.2 x 0.8 x 0.5 x 0.5 x 0.5 x 0.5 = 0.0008 N1 N1 N2 N2 N3 = 0.6 x 0.4 x 0.2 x 0.8 x 0.5 = 0.192
- 19. This accommodates for pronunciations such as: t ow m aa t ow - British English t ah m ey t ow - American English t ah mey t a - Possibly pronunciation when speaking quickly
- 20. With sentences such as: I like apple juice I like tomato juice I hate apple juice I hate tomato juice - Very probable - Very improbable! - Relatively improbable - Relatively probable
- 21. • The Markov Model makes the Speech Recognition systems more intelligent i.e. it can accurately differentiate between similar sounding words like in the case : James's school... James is cool • In simpler Markov models , the state is directly visible to the observer. • In a hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible.
- 22. PERFORMANCE OF A SR SYSTEM • Accuracy is usually rated with word error rate (WER), whereas speed is measured with the real time factor. • Other measures of accuracy include Single Word Error Rate (SWER) and Command Success Rate (CSR).
- 23. Factors affecting the accuracy of a SR system :• • • • • • Vocabulary size and confusability Speaker dependence vs. independence Isolated, discontinuous, or continuous speech Task and language constraints Read vs. spontaneous speech Adverse conditions
- 24. APPLICATIONS • Health Care • Military - High Performance Aircrafts - Air Traffic Control Systems • Telephony – Smart-phones - Customer Helpline Services • Personal Computers
- 25. SIRI AND GOOGLE NOW Intelligent Personal Assistant developed by Apple. Google Now is an intelligent personal assistant developed by Google. Both use a combination of speaker- dependent and speaker-independent sr systems
- 26. CONCLUSION • Speech Recognition systems are an indispensable part of the ever-advancing field of humancomputer interaction. • Needs greater research to tackle various challenges. Thank You!