G2G: Tts-driven Pronunciation Learning For Graphemic Hybrid ASR
2019 · Duc Le, Thilo Koehler, Christian Fuegen, et al.
Abstract
Grapheme-based acoustic modeling has recently been shown to outperform phoneme-based approaches in both hybrid and end-to-end automatic speech recognition (ASR), even on non-phonemic languages like English. However, graphemic ASR still has problems with rare long-tail words that do not follow the standard spelling conventions seen in training, such as entity names. In this work, we present a novel method to train a statistical grapheme-to-grapheme (G2G) model on text-to-speech data that can rewrite an arbitrary character sequence into more phonetically consistent forms. We show that using G2G to provide alternative pronunciations during decoding reduces Word Error Rate by 3% to 11% relative over a strong graphemic baseline and bridges the gap on rare name recognition with an equivalent phonetic setup. Unlike many previously proposed methods, our method does not require any change to the acoustic model training procedure. This work reaffirms the efficacy of grapheme-based modeling and s
Authors
(none)
Tags
Stats
Related papers
- Liteg2p: A Fast, Light And High Accuracy Model For Grapheme-to-phoneme Conversion (2023)5.84
- Data-driven Grapheme-to-phoneme Representations For A Lexicon-free Text-to-speech (2024)4.52
- R-g2p: Evaluating And Enhancing Robustness Of Grapheme To Phoneme Conversion By Controlled Noise Introducing And Contextual Information Incorporation (2022)7.50
- A Two-stage Transliteration Approach To Improve Performance Of A Multilingual ASR (2024)0.00
- Massively Multilingual Neural Grapheme-to-phoneme Conversion (2017)9.76
- One Model To Pronounce Them All: Multilingual Grapheme-to-phoneme Conversion With A Transformer Ensemble (2020)0.00
- Improving Grapheme-to-phoneme Conversion Through In-context Knowledge Retrieval With Large Language Models (2024)2.26
- From Senones To Chenones: Tied Context-dependent Graphemes For Hybrid Speech Recognition (2019)0.00