Shape vector characterization of Vietnamese tones and application to automatic recognition

The tone recognition for Vietnamese standard language (Hanoi dialect) is described. The wavelet method is used to extract the pitch (F0) from a speech signal corpus. Thus, one feature vector for tone recognition of Vietnamese is proposed. Hidden Markov models (HMMs) are then used to recognize the tones. Our results show that tone recognition seems independent of the vowel but presents better accuracy if one of both monotonous tones is used as the pitch reference base. Finally, a first try of a completely isolated word recognition engine, adapted for Vietnamese, is presented.