  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.
