Benoit Brard
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Data-Driven Parametric Text Normalization: Rapidly Scaling Finite-State Transduction Verbalizers to New Languages
Kim Anne Heiligenstein
Nikos Bampounis
Christian Schallhart
Jonas Fromseier Mortensen
Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020), Language Resources and Evaluation Conference (LREC 2020), Marseille, 218–225
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This paper presents a methodology for rapidly generating FST-based verbalizers for ASR and TTS systems by efficiently sourcing language-specific data. We describe a questionnaire which collects the necessary data to bootstrap the number grammar induction system and parameterize the verbalizer templates described in Ritchie et al. (2019), and a machine-readable data store which allows the data collected through the questionnaire to be supplemented by additional data from other sources. We also discuss the benefits of this system for low-resource languages.
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Unified Verbalization for Speech Recognition & Synthesis Across Languages
Richard Sproat
Christian Schallhart
Nikos Bampounis
Jonas Fromseier Mortensen
Millie Holt
Proceedings of Interspeech 2019
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We describe a new approach to converting written tokens to their spoken form, which can be used across automatic speech recognition (ASR) and text-to-speech synthesis (TTS) systems. Both ASR and TTS systems need to map from the written to the spoken domain, and we present an approach that enables us to share verbalization grammars between the two systems. We also describe improvements to an induction system for number name grammars. Between these shared ASR/TTS verbalization systems and the improved induction system for number name grammars, we see significant gains in development time and scalability across languages
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