LLM-Powered Grapheme-to-Phoneme Conversion: Benchmark and Case Study
Grapheme-to-phoneme (G2P) conversion is critical in speech processing,
particularly for applications like speech synthesis. G2P systems must possess
linguistic understanding and contextual awareness of languages with polyphone
words and context-dependent phonemes. Large language models (LLMs) have
recently demonstrated significant potential in various language tasks,
suggesting that their phonetic knowledge could be leveraged for G2P. In this
paper, we evaluate the performance of LLMs in G2P conversion and introduce
prompting and post-processing methods that enhance LLM outputs without
additional training or labeled data. We also present a benchmarking dataset
designed to assess G2P performance on sentence-level phonetic challenges of the
Persian language. Our results show that by applying the proposed methods, LLMs
can outperform traditional G2P tools, even in an underrepresented language like
Persian, highlighting the potential of developing LLM-aided G2P systems.
66e9c55095c7a0c91b804500
Mahta Fetrat Qharabagh
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2024-09-18T07:48:51.629Z
657c45f6429a20edb520d7a8
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Mahta Fetrat
user
MahtaFetrat
66e9c55095c7a0c91b804501
Zahra Dehghanian
66e9c55095c7a0c91b804502
Hamid R. Rabiee
LLM-Powered Grapheme-to-Phoneme Conversion: Benchmark and Case Study
Grapheme-to-phoneme (G2P) conversion is critical in speech processing,
particularly for applications like speech synthesis. G2P systems must possess
linguistic understanding and contextual awareness of languages with polyphone
words and context-dependent phonemes. Large language models (LLMs) have
recently demonstrated significant potential in various language tasks,
suggesting that their phonetic knowledge could be leveraged for G2P. In this
paper, we evaluate the performance of LLMs in G2P conversion and introduce
prompting and post-processing methods that enhance LLM outputs without
additional training or labeled data. We also present a benchmarking dataset
designed to assess G2P performance on sentence-level phonetic challenges of the
Persian language. Our results show that by applying the proposed methods, LLMs
can outperform traditional G2P tools, even in an underrepresented language like
Persian, highlighting the potential of developing LLM-aided G2P systems.
LLMs can perform G2P conversion more accurately than traditional tools, especially for underrepresented languages like Persian, using prompting and post-processing methods without additional training or labeled data.
grapheme-to-phoneme G2P large language models LLMs phonetic knowledge prompting post-processing benchmarking dataset Persian language underrepresented language