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Poster
Open access
English

Integrating Grammar-Based Language Models into Domain-Specific ASR Systems

Presented at2nd Advanced Language Processing School (ALPS), Online, 17.01.2022
Presentation date2022-01-17
Abstract

Language Models (LMs) represent a crucial component in the architecture of Automatic Speech Recognition (ASR) systems. Current trends in this area point to the creation of high-performing and increasingly robust systems through the exploitation of large amounts of data.

Even though the use of corpus-based models proves to be a dominant strategy for language modelling, it may not be the most suitable approach in some of today’s ASR applications. This is especially evident in domains where there is a strong interest in controlling the hypotheses generated by the system and producing only reliable outputs.

Providing a deliberately constrained transcription can be more effectively achieved using a formal approach, and thus with the use of grammars, which ultimately contribute to better capturing the inherent structures of the target language.

For these reasons, we present a tool that allows to efficiently integrate regular grammars as LMs in Kaldi, a widely used toolkit for speech recognition research. To the best of our knowledge, there is currently no existing tool that performs this task. We thus make it freely available along with some demo examples and crowdsourced evaluation corpora so that it can be used by researchers or developers in their own experiments and applications.

eng
Keywords
  • Language modelling
  • Automatic speech recognition
Citation (ISO format)
ORMAECHEA GRIJALBA, Lucia. Integrating Grammar-Based Language Models into Domain-Specific ASR Systems. In: 2nd Advanced Language Processing School (ALPS). Online. 2022.
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Poster
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Identifiers
  • PID : unige:162415
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Technical informations

Creation07/15/2022 10:36:00 AM
First validation07/15/2022 10:36:00 AM
Update time03/16/2023 7:05:12 AM
Status update03/16/2023 7:05:12 AM
Last indexation02/01/2024 8:31:51 AM
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