Language Accent Classi cation in American EnglishxLevent

It is well known that speaker variability caused by accent is one factor that degrades performance of speech recognition algorithms. If knowledge of speaker accent can be estimated accurately, then a modiied set of recognition models which addresses speaker accent could be employed to increase recognition accuracy. In this study, the problem of language accent classiication in American English is considered. A database of foreign language accent is established that consists of words and phrases that are known to be sensitive to accent. Next, isolated word and phoneme based accent classiication algorithms are developed. The feature set under consideration includes Mel-cepstrum coeecients and energy, and their rst order diierences. It is shown that as test utterance length increases, higher classiication accuracy is achieved. Isolated word strings of 7-8 words uttered by the speaker results in an accent classiication rate of 93% among four diierent language accents. A subjective listening test is also conducted in order to compare human performance with computer algorithm performance in accent discrimination. The results show that computer based accent classiication consistently achieves superior performance over human listener responses for classiication. 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 innuence of foreign accent on speech recognition algorithms. It is shown that training separate models for each accent rather than using a single model for each word can improve recognition accuracy dramatically. PACS Code 43.72 z Permission is hereby granted to publish this abstract separately.