TRANSCRIPTION OF MULTI-GENRE MEDIA ARCHIVES USING OUT-OF-DOMAIN DATA

被引：0

作者：

Bell, P. J. ^{[1
]}

Gales, M. J. F.

Lanchantin, P.

Liu, X.

Long, Y.

Renals, S. ^{[1
]}

Swietojanski, P. ^{[1
]}

Woodland, P. C.

机构：

[1] Univ Edinburgh, Ctr Speech Technol Res, Edinburgh EH8 9AB, Midlothian, Scotland

来源：

2012 IEEE WORKSHOP ON SPOKEN LANGUAGE TECHNOLOGY (SLT 2012) | 2012年

基金：

英国工程与自然科学研究理事会;

关键词：

speech recognition; tandem; cross-domain adaptation; media archives;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We describe our work on developing a speech recognition system for multi-genre media archives. The high diversity of the data makes this a challenging recognition task, which may benefit from systems trained on a combination of in-domain and out-of-domain data. Working with tandem HMMs, we present Multi-level Adaptive Networks (MLAN), a novel technique for incorporating information from out-of-domain posterior features using deep neural networks. We show that it provides a substantial reduction in WER over other systems, with relative WER reductions of 15% over a PLP baseline, 9% over in-domain tandem features and 8% over the best out-of-domain tandem features.

引用

页码：324 / 329

页数：6

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