Lightly supervised and data-driven approaches to Mandarin broadcast news transcription
Berlin Chen, Jen-Wei Kuo, Wen-Hung Tsai · 2004
This paper investigates the use of several lightly supervised and data-driven approaches to Mandarin broadcast news transcription. First, with a consideration of the special structural properties of the Chinese language, a fast acoustic took-ahead technique for estimating the unexplored part of speech utterance was integrated into the lexical tree search to improve the search efficiency, in conjunction with the conventional language model look-ahead technique. Then, a verification-based method for automatic acoustic training data acquisition was developed to make use of the large amount of untranscribed speech data. Finally, two alternative strategies for language model adaptation were further studied for accurate language model estimation. With the above approaches, the system yielded an 11.94% character error rate on the Mandarin broadcast news collected in Taiwan.