Phoneme recognition with kernel learning algorithms

Abstract

An isolated phoneme recognition system is proposed using time-frequency domain analysis and support vector machines (SVMs). The TIMIT corpus which contains a total of 6300 sentences, ten sentences spoken by each of 630 speakers from eight major dialect regions of the United States, was used in this experiment. Provided time-aligned phonetic transcription was used to extract phonemes from speech samples. A 55-output classifier system was designed corresponding to 55 classes of phonemes and trained with the kernel learning algorithms. The training dataset was extracted from clean training samples. A portion of the database, i.e., 65<th>338 samples of training dataset, was used to train the system. The performance of the system on the training dataset was 76.4%. The whole test dataset of the TIMIT corpus was used to test the generalization of the system. All samples, i.e., 55<th>655 samples of the test dataset, were used to test the system. The performance of the system on the test dataset was 45.3%. This approach is currently under development to extend the algorithm for continuous phoneme recognition. [Work supported in part by grants from DARPA, NASA, and ONR.]