Abstract

There are eight major dialectal regions in addition to Mandarin (Northern China) in China, which can be further divided into more than 40 sub-categories. Although the Chinese dialects share a written language and standard Chinese (Putonghua) is widely spoken in most regions, speech is still strongly influenced by the native dialects. This great linguistic diversity poses problems for automatic speech and language technology. Automatic speech recognition relies to a great extent on the consistent pronunciation and usage of words within a language. In Chinese, word usage, pronunciation, and syntax and grammar vary depending on the speaker’s dialect. As a result speech recognition systems constructed to process standard Chinese (Putonghua) perform poorly for the great majority of the population.

Efforts have been made as in JHU Summer Workshop 2004 to develop a general framework to model phonetic, lexical, and pronunciation variability in dialectal Chinese automatic speech recognition tasks. The goal was/is to find suitable methods that employ dialect-related knowledge and training data (in relatively small quantities) to modify the baseline standard Chinese Recognizer to obtain a dialectal Chinese recognizer for the specific dialect of interest.

In this talk, work done in JHU Summer Workshop 2004 as well as that in the following years will be introduced, covering several aspects such as Dialectal Chinese database collection (for Wu, Min, Yue, Chuan and so on), Dialectal Lexicon Construction, and acoustic modeling.