کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
558326 874902 2013 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Language model cross adaptation for LVCSR system combination
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
Language model cross adaptation for LVCSR system combination
چکیده انگلیسی

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple sub-systems that may even be developed at different sites. Cross system adaptation, in which model adaptation is performed using the outputs from another sub-system, can be used as an alternative to hypothesis level combination schemes such as ROVER. Normally cross adaptation is only performed on the acoustic models. However, there are many other levels in LVCSR systems’ modelling hierarchy where complimentary features may be exploited, for example, the sub-word and the word level, to further improve cross adaptation based system combination. It is thus interesting to also cross adapt language models (LMs) to capture these additional useful features. In this paper cross adaptation is applied to three forms of language models, a multi-level LM that models both syllable and word sequences, a word level neural network LM, and the linear combination of the two. Significant error rate reductions of 4.0–7.1% relative were obtained over ROVER and acoustic model only cross adaptation when combining a range of Chinese LVCSR sub-systems used in the 2010 and 2011 DARPA GALE evaluations.


► Language model cross adaptation was investigated.
► Powerful framework for improving LVCSR system combination.
► Stand-alone or combined with acoustic model cross adaptation.
► Significant error rate reductions of 4.0–7.1% relative.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Speech & Language - Volume 27, Issue 4, June 2013, Pages 928–942
نویسندگان
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