Amadou
- 1. Field: Computational linguistics Topic :Speech Recognition how SR came to be and how it works? How beneficial is it for students? Presenter: AMADOU ADAMOU AMINOU
- 2. Have you ever talked to your computer or your phone? Where it actually recognized what you said and than did something as a result??
- 3. Contents • History • Introduction • Types of Speech Recognition • Components of SR • Applications • Examples of SR • Weakness and flaws • Conclusion • References
- 4. INTRODUCTION WHAT IS SPEECH RECOGNISTION? • 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. • SR simply is the process of converting spoken input to text. PELLOM B. Sonic: The University of Colorado Continuous, Speech Recognition System.2001
- 5. History • The first speech recognition system was invented by Bell laboratories in 1952 in US. It could understand only digits spoken by a single voice. The system is called the “AUDREY” system. • Ten years late labs in the US, Japan, England developed hardware dedicated to recognize spoken sounds. • They now have large vocabularies and can recognize continuous speech. Eugene Weinstein - Speech Recognition p15, Prentice hall 1995
- 6. Why was SRS invented • Individuals With Disabilities – Assists those who have visual impairment, hand immobility, dyslexia, etc. • Medical Transcription – Reduces delays to write out medical transcriptions • Dictation - Converts words to text in emails or other word documents (also helpful for English Language Learners). • Access Menu Commands – Opens files using voice commands. Eugene Weinstein - Speech Recognition p40-50, Prentice hall 1995
- 7. Speech recognition system consists of: • A microphone. • A speech recognition software. • A computer to take and interpret the speech. • A good quality soundcard for input and output. • A proper and good pronunciation. BGROUV, B T. (1989) computational linguistics, London, Longman
- 8. Two types of SR.. • Speaker-dependent systems – Require “training” to “teach” the individual System – More robust – But less convenient – And obviously less portable • Speaker-independent systems – Language coverage is reduced to compensate need to be flexible in phoneme identification – Clever compromise is to learn on the fly Eugene Weinstein - Speech Recognition p40-45, Prentice hall 1995
- 9. Components • Audio input • Grammar • Speech Recognition Engine • Acoustic Model • Recognized text
- 10. What’s hard about that? • Digitization – Converting analogue signal into digital representation. • Signal processing – Separating speech from background noise. • Phonetics – Variability in human speech. • Phonology – Recognizing individual sound distinctions (similar phonemes.) • Lexicology and syntax – Disambiguating homophones. – Features of continuous speech. • Syntax and pragmatics – Interpreting features. – Filtering of performance errors (disfluencies). Eugene Weinstein - Speech Recognition p15-67, Prentice hall 1995.
- 11. Potential uses in education • Teaching students of foreign languages to pronounce vocabulary correctly • Enabling students who are physically handicapped who cant use keyboard. • Enabling student with textual interpretive problems e.g. Dyslexia to enter text verbally. • Restrictive access on high security computer, where a keyboard may be used by hackers. BGROUV, B T. (1989) computational linguistics, London, Longman
- 12. Applications of Speech Recognition • Speech recognition applications include controlling any devices in your home (e.g. video), Call routing (e.g., "I would like to make a collect call"), Simple data entry (e.g., entering a credit card number), Preparation of structured documents (e.g., A radiology report), Speech-to-text processing (e.g., word processors or emails) In aircraft cockpits (usually termed Direct Voice Input). BGROUV, B T. (1989) computational linguistics, London: LONMAN
- 13. Example: Microsoft Speech Recognition – Windows 7
- 14. SIRI & GOOGLE 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
- 15. Weakness and Flaws • Low signal-to-noise ratio: the program needs to hear the words spoken distinctly. • Intensive use of device power. • Homonyms e.g. “there” and “their”, “be” and “bee” • Overlapping speech. • No program is 100% perfect • Problem of understanding dialects and accents Example: see video A practical Intro to the computer Analysis of Language, Geoff Barnbrook, 1996, Edinburgh University press.
- 16. Conclusion • Revolutionize the way people conduct business over the Web and ,differentiate world-class e-businesses. • VoiceXML ties speech recognition and telephony together • At some point in the future, speech recognition may become speech understanding. • voice-enabled Web solutions TODAY!
- 17. References • PELLOM, B., Sonic: The University of Colorado Continuous Speech Recognition System, 2001 • BOGUREV, B T. (1989) computational linguistics, London: LONGMAN • Eugene Weinstein - Speech Recognition, Prentice hall 1995 • http://www.tldp.org/HOWTO/Speech- Recognition- • A practical Intro to the computer Analysis of Language, Geoff BRANKROOK, 1996, Edinburgh University press.