TONE RECOGNITION FOR CONTINUOUS ACCENTED MANDARIN CHINESE

In this paper, the ability of human listeners to recognize tones from continuous Mandarin Chinese is evaluated and compared to the accuracy of automatic systems for tone classification and recognition. All tones used for experimentation were extracted from the RASC863 continuous Mandarin Chinese database. The human listeners are native speakers of Mandarin and the automatic methods consist of tone classification using neural networks and tone recognition using Hidden Markov Models. Features used for the automatic methods are a combination of spectral/temporal features, energy contours, and pitch contours. When very little context is used (i.e., vowel segments only) the human and machine performance is comparable. However, as the context interval is increased, the human performance is much better than the best machine performance obtained.