Chinese dialect identification using an acoustic-phonotactic model

In this paper we develop hidden Markov model (HMM) based approaches to identify Chinese dialects spoken in Taiwan. This task can be aided by exploiting various characteristic features of Chinese spoken languages. The baseline system performs phonotactic analysis after the speech utterance is tokenized into a sequence of five broad phonetic classes. The sequential statistics of the resulting symbols are then used to distinguish one dialect from another. The second approach we tested is to incorporate dialect-dependent phonotactic constraints into the phonetic tokenization rather than applying these constraints after the broad phonetic classification is complete. These algorithms were evaluated using a multispeaker speech corpus of text-independent spontaneous speech data. Simulation results indicate that the acousticphonotactic approach to dialect identification yields better performance with an average identification rate of 89.6%, compared to 70% for the baseline system.