Data-driven lexicon refinement using local and web resources for Chinese speech recognition

This paper proposes a data-driven lexicon refinement method. By expanding and polishing lexicon using local and web resources, accuracy of Chinese automatic speech recognition (ASR) system is boosted effectively. The proposed lexicon refining process is composed of two steps. First, an improved intra-word measure is introduced. It helps to expand lexicon from local text corpora. Second, the expanded lexicon is polished by enumerating the popularity of appended words based on web query results via search engine. The evaluation experiments are carried out on an application of voice-enabled tourist information query system. Experimental results show that the proposed lexicon refinement method reduces character error rate (CER) by 7.9% relatively.