AUTOMATIC EVALUATION OF ENGLISH PRONUNCIATION BASED ON SPEECH RECOGNITION TECHNIQUES
A new method is proposed for automatically evaluating the English pronunciation quality of non-native speakers. It is assumed that pronunciation can be rated using three criteria: the static characteristics of phonetic spectra, the dynamic structure of spectrum sequences, and the prosodic characteristics of utterances. The evaluation uses speech recognition techniques to compare the English words pronounced by a non-native speaker with those pronounced by a native speaker. Three evaluation measures are proposed to rate pronunciation quality. (1) The standard deviation of the mapping vectors, which map the codebook vectors of the non-native speaker onto the vector space of the native speaker, is used to evaluate the static phonetic spectra characteristics. (2) The spectral distance between words pronounced by the non-native speaker and those pronounced by the native speaker obtained by the DTW method is used to evaluate the dynamic characteristics of spectral sequences. (3) The differences in fundamental frequency and speech power between the pronunciation of the native and non-native speaker are used as the criteria for evaluating prosodic characteristics. Evaluation experiments are carried out using 441 words spoken by 10 Japanese speakers and 10 native speakers. One half of the 441 words was used to evaluate static phonetic spectra characteristics, and the other half was used to evaluate the dynamic characteristics of spectral sequences, as well as the prosodic characteristics. Based on the experimental results, the correlation between the evaluation scores and the scores determined by human judgement is found to be 0.90.
english pronunciation speech recognition speech processing speaker adaptation education