Exploiting polynomial-fit histogram equalization and temporal average for robust speech recognition

Shih Hsiang Lin*, Yao Ming Yeh, Berlin Chen

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

14 引文 斯高帕斯(Scopus)

摘要

The performance of current automatic speech recognition (ASR) systems radically deteriorates when the input speech is corrupted by various kinds of noise sources. Quite a few of techniques have been proposed to improve ASR robustness in the past several years. Histogram equalization (HEQ) is one of the most efficient techniques that have been used to compensate the nonlinear distortion. In this paper, we explored the use of the data fitting scheme to efficiently approximate the inverse of the cumulative density function of training speech for HEQ, in contrast to the conventional table-lookup or quantile based approaches. Moreover, the temporal average operation was also performed on the feature vector components to alleviate the influence of sharp peaks and valleys that were caused by non-stationary noises. Finally, we also investigated the possibility of combining our approaches with other feature discrimination and decorrelation methods. All experiments were carried out on the Aurora-2 database and task. Encouraging results were initially demonstrated.

原文英語
主出版物標題INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP
發行者International Speech Communication Association
頁面2522-2525
頁數4
ISBN(列印)9781604234497
出版狀態已發佈 - 2006 一月 1
事件INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP - Pittsburgh, PA, 美国
持續時間: 2006 九月 172006 九月 21

出版系列

名字INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP
5

其他

其他INTERSPEECH 2006 and 9th International Conference on Spoken Language Processing, INTERSPEECH 2006 - ICSLP
國家/地區美国
城市Pittsburgh, PA
期間2006/09/172006/09/21

ASJC Scopus subject areas

  • 電腦科學(全部)

指紋

深入研究「Exploiting polynomial-fit histogram equalization and temporal average for robust speech recognition」主題。共同形成了獨特的指紋。

引用此