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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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portfolio
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publications
Improving Grapheme-to-Phoneme Conversion through In-Context Knowledge Retrieval with Large Language Models
Published in International Symposium on Chinese Spoken Language Processing (ISCSLP), 2024
Grapheme-to-phoneme (G2P) conversion is a crucial step in Text-to-Speech (TTS) systems, responsible for mapping grapheme to corresponding phonetic representations. However, it faces ambiguities problems where the same grapheme can represent multiple phonemes depending on contexts, posing a challenge for G2P conversion. Inspired by the remarkable success of Large Language Models (LLMs) in handling context-aware scenarios, contextual G2P conversion systems with LLMs’ in-context knowledge retrieval (ICKR) capabilities are proposed to promote disambiguation capability. The efficacy of incorporating ICKR into G2P conversion systems is demonstrated thoroughly on the Librig2p dataset. In particular, the best contextual G2P conversion system using ICKR outperforms the baseline with weighted average phoneme error rate (PER) reductions of 2.0% absolute (28.9% relative). Using GPT-4 in the ICKR system can increase of 3.5% absolute (3.8% relative) on the Librig2p dataset.
Recommended citation: Han, Dongrui, et al. "Improving grapheme-to-phoneme conversion through in-context knowledge retrieval with large language models." 2024 IEEE 14th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2024.
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Imitation Learning for Elder-Facing Speech Synthesis
Published in Interspeech, 2026
Recent advances in text-to-speech (TTS) synthesis have achieved highly natural and expressive speech generation. However, these systems are designed for general adults and overlook older adults’ speech comprehension needs due to age-related sensory and cognitive decline. Prior work involves older adults by collecting preference feedback to tune model parameters. However, obtaining sufficient preference data is costly and difficult, as older adults quickly become fatigued during collection. In this paper, we propose a novel imitation learning (IL) framework to learn TTS models from expert demonstrations. We further improve Group Relative Policy Optimization (GRPO) with on-policy reward learning (OPRL) to mitigate reward hacking under limited supervision from expert demonstrations. Experimental results show that GRPO w/ OPRL outperforms standard GRPO and supervised baselines in objective and subjective metrics.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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