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Chinese Dialect Classification Using Acoustic Universal Structure in Speech

It is widely accepted that the performance of modern Automatic Speech Recognition (ASR) systems is strongly affected by the variations between training data and test data. Among these variations, some are caused by non-linguistic factors, such as the physical features of the speakers, the different recording equipments, environments and so on. Meanwhile, some of the variations are also caused by some linguistic factors, for example, the accent and dialect of languages, which always exhibit some regional regularity. Nowadays, in order to deal with these variations to enhance the performance of the ASR systems, a great number of frameworks and algorithms are proposed and some satisfying results are already obtained. But in some particular cases, such as the dialect classification, none of these methods is suitable because they can’t distinguish well the linguistic variations from the variations caused by non-linguistic factors. In China, there are hundreds of dialects and subdialects which are more or less different to each other lexically, phonologically and phonetically. Then the classification of these dialects becomes a challenge especially considering that the variations caused by dialectal factors are acoustically mixed with the non-linguistic variations. In this paper, after introducing the special dialectal situation in China, a new method is proposed to classify dialect speakers by their acoustic structures which are build on their utterances of 9 selected monophthong characters. Then, classification experiment is carried out and the result is discussed.