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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Woosung Kim Khudanpur, S. |
| Copyright Year | 2004 |
| Description | Author affiliation: Center for Language & Speech Process., Johns Hopkins Univ., Baltimore, MD, USA (Woosung Kim; Khudanpur, S.) |
| Abstract | Statistical language model estimation requires large amounts of domain-specific text, which is difficult to obtain in many languages. We propose techniques which exploit domain-specific text in a resource-rich language to adapt a language model in a resource-deficient language. A primary advantage of our technique is that in the process of cross-lingual language model adaptation, we do not rely on the availability of any machine translation capability. Instead, we assume that only a modest-sized collection of story-aligned document-pairs in the two languages is available. We use ideas from cross-lingual latent semantic analysis to develop a single low-dimensional representation shared by words and documents in both languages, which enables us to (i) find documents in the resource-rich language pertaining to a specific story in the resource-deficient language, and (ii) extract statistics from the pertinent documents to adapt a language model to the story of interest. We demonstrate significant reductions in perplexity and error rates in a Mandarin speech recognition task using this technique. |
| File Size | 273235 |
| File Format | |
| ISBN | 0780384849 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2004.1325971 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-05-17 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Natural languages Automatic speech recognition Speech processing Natural language processing Information retrieval Adaptation model Availability Statistical analysis Error analysis Speech recognition |
| Content Type | Text |
| Resource Type | Article |
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