| Summary: | 碩士 === 國立交通大學 === 電信工程研究所 === 100 === This thesis presents an ASR system based on Weighted Finite-State Transducer(WFST). In the first we will introduce some algorithms that used to construct WFST graph, and how we express different models using WFST format. Then, we described a hierarchical language model training by NER labels to deal with the problem of chinese person name, which often detected as OOV words. We incorporating the hierachical langugage model into one-stage and two-stage ASR system. In the one-stage ASR system, an on-the-fly replace algoritm was implemented to reduce the memory’s allocation, so we can use a complex hierachical model to calculate the probability of chinese person name. We evaluate our approach on the TCC-300 corpus, which consists of long paragraphic utterances, obtained a 0.38% absolute improvement in word error rate in one-stage ASR system; and at most 2.91% when using two-stage ASR system.
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