LANGUAGE ACCENT CLASSIFICATION IN AMERICAN ENGLISH

It is well known that speaker variability caused by accent is one fact or that degrades performance of speech recognition algorithms. If know ledge of speaker accent can be estimated accurately, then a modified s et of recognition models which addresses speaker accent could be emplo yed to increase recognition accuracy. In this study, the problem of la nguage accent classification in American English is considered. A data base of foreign language accent is established that consists of words and phrases that are known to be sensitive to accent. Next, isolated w ord and phoneme based accent classification algorithms are developed. The feature set under consideration includes Mel-cepstrum coefficients and energy, and their first order differences. It is shown that as te st utterance length increases, higher classification accuracy is achie ved, Isolated word strings of 7-8 words uttered by the speaker results in an accent classification rate of 93% among four different language accents, A subjective listening test is also conducted in order to co mpare human performance with computer algorithm performance in accent discrimination. The results show that computer based accent classifica tion consistently achieves superior performance over human listener re sponses for classification. It is shown, however, that some listeners are able to match algorithm performance for accent detection. Finally, an experimental study is performed to investigate the influence of fo reign accent on speech recognition algorithms. It is shown that traini ng separate models for each accent rather than using a single model fo r each word can improve recognition accuracy dramatically.