Seed Words Based Data Selection for Language Model Adaptation
This work addresses the incremental improvement of ASR systems for specialized domains like dentistry, where managing domain-specific terms is crucial.
The paper tackles the problem of adapting language models for automatic speech recognition to domain-specific terminology, such as dentistry, by automatically selecting sentences from a text corpus that match a user-provided glossary. Results show effectiveness in reducing out-of-vocabulary rates and word error rates using metrics like OOV rate and WER.
We address the problem of language model customization in applications where the ASR component needs to manage domain-specific terminology; although current state-of-the-art speech recognition technology provides excellent results for generic domains, the adaptation to specialized dictionaries or glossaries is still an open issue. In this work we present an approach for automatically selecting sentences, from a text corpus, that match, both semantically and morphologically, a glossary of terms (words or composite words) furnished by the user. The final goal is to rapidly adapt the language model of an hybrid ASR system with a limited amount of in-domain text data in order to successfully cope with the linguistic domain at hand; the vocabulary of the baseline model is expanded and tailored, reducing the resulting OOV rate. Data selection strategies based on shallow morphological seeds and semantic similarity viaword2vec are introduced and discussed; the experimental setting consists in a simultaneous interpreting scenario, where ASRs in three languages are designed to recognize the domain-specific terms (i.e. dentistry). Results using different metrics (OOV rate, WER, precision and recall) show the effectiveness of the proposed techniques.