Alternative hypothesis generation using a weighted kernel feature matrix for ASR substitution error correction

Chao Hong Liu, Chung-Hsien Wu, David Sarwono

研究成果: Conference contribution

摘要

Although automatic speech recognition (ASR) has been successfully used in several applications, it is still non-robust and imprecise especially in a harsh environment wherein the input speech is of low quality. Robust error correction for ASR outputs thus becomes important in addition to improving recognition performance. In recent approaches to error correction, linguistic or domain information is used to generate the alternative hypotheses for the ASR outputs followed by the selection of the most likely alternative. In this study, the distances between ASR outputs and the potentially correct alternatives are estimated based on a weighted context-dependent syllable cluster-based kernel feature matrix followed by multidimensional scaling (MDS)-based distance rescaling. These distances are then used to construct an alternative syllable lattice and the dynamic programming is used to obtain the most likely correct output with respect to the original ASR results. Experiments show that the proposed method achieved about 1.95% improvement on the word error rate compared to the correction pair approach using the MATBN Mandarin Chinese broadcast news corpus.

原文English
主出版物標題2012 8th International Symposium on Chinese Spoken Language Processing, ISCSLP 2012
頁面1-5
頁數5
DOIs
出版狀態Published - 2012 十二月 1
事件2012 8th International Symposium on Chinese Spoken Language Processing, ISCSLP 2012 - Hong Kong, China
持續時間: 2012 十二月 52012 十二月 8

出版系列

名字2012 8th International Symposium on Chinese Spoken Language Processing, ISCSLP 2012

Other

Other2012 8th International Symposium on Chinese Spoken Language Processing, ISCSLP 2012
國家China
城市Hong Kong
期間12-12-0512-12-08

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Linguistics and Language

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