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 Light background will be provided

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

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

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

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

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

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

Serbo-Croatian Vowel Chart

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

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

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

Results Results for Thai (previous work) Results for SC (present work) MLPSVMMLPSVM 92.28% Recognition rate 94.99% Recognition rate (DDAG) 90-95% Recognition rate Work in progres

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