Lightly Supervised and Data-Driven Approaches to Mandarin Broadcast News Transcription

This article investigates the use of several lightly supervised and data-driven approaches to Mandarin broadcast news transcription. With the special structural properties of the Chinese language taken into consideration, a fast acoustic look-ahead technique for estimating the unexplored part of a speech utterance is integrated into lexical tree search to improve search efficiency. This technique is used in conjunction with the conventional language model look-ahead technique. Then, a verification-based method for automatic acoustic training data acquisition is proposed to make use of large amounts of untranscribed speech data. Finally, two alternative strategies for language model adaptation are studied with the goal of achieving accurate language model estimation. With the above approaches, the overall system was found in experiments to yield an 11.88% character error rate when applied to Mandarin broadcast news collected in Taiwan.