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Neural Net Algorithms for SC Vowel Recognition

Neural Net Algorithms for SC Vowel Recognition. Presentation for EE645 Neural Networks and Learning Algorithms Spring 2003 Diana Stojanovic. Summary. Neural net algorithms applied to recognition of Serbo-Croatian vowels Follows Thubthong & Kijsirkul (2001) paper on Thai phoneme recognition

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Neural Net Algorithms for SC Vowel Recognition

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  1. Neural Net Algorithms for SC Vowel Recognition Presentation for EE645 Neural Networks and Learning Algorithms Spring 2003 Diana Stojanovic

  2. Summary • Neural net algorithms applied to recognition of Serbo-Croatian vowels • Follows Thubthong & Kijsirkul (2001) paper on Thai phoneme recognition • Light background will be provided

  3. Introduction • Speech recognition has many applications (PCs, cell phones, home appliance activation a la Dilbert etc.)

  4. Introduction 2 • There are various algorithms for recognizing speech, some of which rely on the recognition of individual phonemes or sounds

  5. Block diagram of speech recognition system For this project Signal Processing: segmentation, spectral analysis Speech Recognition: Individual vowel recognition Signal Processing Speech Recognition

  6. Previous work • Thubthong & Kijsirkul (2001) tested multi-class Support Vector Machine (SVM) vs. Multilayer Perceptron (MLP) for recognition of Thai Vowels and tones • They claim superiority of SVM, while the recognition rate differs by 2-3% for comparably complex systems

  7. About speech sounds • Speech sound is an acoustic wave • Speaker’s vocal tract shapes the spectrum of each sound • Spectrum depends on the speaker and on the property of the particular sound (for instance /u/), thus recognition in spectral domain is possible

  8. Vowel Formants • Vowels can be recognized in spectral domain by the characteristic “lines” corresponding to their properties (backness, height, lip rounding etc.) • These “lines” –formants- occur at resonant frequencies of the vocal tract

  9. Serbo-Croatian Vowel Chart

  10. Data Used in the Project Data collection and Properties • Type of speech: speaker dependent, accented syllables • 480 isolated words were recorded and digitized at 11 kHz • Vowels in accented position segmented manually • Vowel formants measured by PCQuirer

  11. Sound Features Measured • Only first two formants were used for training the nets in order to reduce complexity • Based on the property of the SC sounds, the performance should not suffer from this low dimensionality

  12. Perceptron,Backprop and Support Vector Machine • We learned about this throughout the semester . For details, please refer to the paper

  13. Results

  14. What is next? • First, finish the SVM results • Examine fast, connected speech • Speaker independent recognition

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