Unsupervised Cross-Adaptation Approach for Speech Recognition by Combined Language Model and Acoustic Model Adaptation

摘要：

The aim of this study is to improve speech recognition with a combination of language model (LM) and the acoustic model (AM) adaptation. The proposed adaptation techniques are based on cross-system adaptation or cross-validation (CV) adaptation. The principle is to use complementary information derived from several systems or data sets. Because language information and acoustic information differ completely, the combined approach is expected to be effective. We evaluate the performance of the proposed methods by conducting speech recognition experiments using the Corpus of Spontaneous Japanese (CSJ). Both cross-system adaptation and CV adaptation give better performance than the conventional adaptation method; the crosssystem adaptation method was found to exhibit the best recognition performance.

作者: Tetsuo Kosaka Taro Miyamoto Masaharu Kato

作者单位: Graduate School of Science and Engineering, Yamagata University, Yonezawa, Japan

会议类型: 国际会议

会议名称: 2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)

会议地点: 西安

会议语种:英文

页码: 1-4

在线出版日期: 2011-10-18（万方平台首次上网日期，不代表论文的发表时间）