Tied-State Mixture Language Model For Wfst-Based Speech Recognition

13TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2012 (INTERSPEECH 2012), VOLS 1-3(2012)

引用 22|浏览16
暂无评分
摘要
This paper describes a language model combination method for automatic speech recognition (ASR) systems based on Weighted Finite-State Transducers (WFSTs). The performance of ASR in real applications often degrades when an input utterance is out of the domain of the prepared language models. To cover a wide range of domains, it is possible to utilize a combination of multiple language models. To do this, we propose a language model combination method with a two-step approach; it first uses a union operation to incorporate all components into a single transducer and then merges states of the transducer to mix n-grams included in multiple models and to retain unique n-grams in each model simultaneously. The method has been evaluated on speech recognition experiments on travel conversation tasks and has demonstrated improvements in recognition performance.
更多
查看译文
关键词
Language model combination,WFST
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要