Summary of Improving Grapheme-to-phoneme Conversion Through In-context Knowledge Retrieval with Large Language Models, by Dongrui Han et al.
Improving Grapheme-to-Phoneme Conversion through In-Context Knowledge Retrieval with Large Language Models
by Dongrui Han, Mingyu Cui, Jiawen Kang, Xixin Wu, Xunying Liu, Helen Meng
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper focuses on improving Grapheme-to-Phoneme (G2P) conversion, a crucial step in Text-to-Speech systems. Current G2P conversion methods face ambiguities where the same grapheme can represent multiple phonemes depending on contexts. To address this challenge, the authors propose contextual G2P conversion systems that leverage Large Language Models’ (LLMs’) in-context knowledge retrieval (ICKR) capabilities for disambiguation. The study demonstrates the efficacy of incorporating ICKR into G2P conversion systems using the Librig2p dataset, showing a weighted average phoneme error rate (PER) reduction of 2.0% absolute (28.9% relative) compared to the baseline. Using GPT-4 in the ICKR system can further increase PER reductions by 3.5% absolute (3.8% relative). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at converting text into speech sounds. Right now, computers have trouble figuring out which sound a letter or group of letters should make because the same letters can mean different things depending on the context. To solve this problem, scientists came up with a new way to use special computer models that are really good at understanding language and its nuances. They tested their idea using a large dataset and found that it worked much better than previous methods. This could lead to more realistic and natural-sounding text-to-speech systems in the future. |
Keywords
» Artificial intelligence » Gpt
