PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation
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| Format: | Preprint |
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2025
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| _version_ | 1866911138501885952 |
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| author | He, Jiajun Sawada, Naoki Miyazaki, Koichi Toda, Tomoki |
| author_facet | He, Jiajun Sawada, Naoki Miyazaki, Koichi Toda, Tomoki |
| contents | Automatic speech recognition (ASR) systems struggle with domain-specific named entities, especially homophones. Contextual ASR improves recognition but often fails to capture fine-grained phoneme variations due to limited entity diversity. Moreover, prior methods treat entities as independent tokens, leading to incomplete multi-token biasing. To address these issues, we propose Phoneme-Augmented Robust Contextual ASR via COntrastive entity disambiguation (PARCO), which integrates phoneme-aware encoding, contrastive entity disambiguation, entity-level supervision, and hierarchical entity filtering. These components enhance phonetic discrimination, ensure complete entity retrieval, and reduce false positives under uncertainty. Experiments show that PARCO achieves CER of 4.22% on Chinese AISHELL-1 and WER of 11.14% on English DATA2 under 1,000 distractors, significantly outperforming baselines. PARCO also demonstrates robust gains on out-of-domain datasets like THCHS-30 and LibriSpeech. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_04357 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation He, Jiajun Sawada, Naoki Miyazaki, Koichi Toda, Tomoki Computation and Language Artificial Intelligence Machine Learning Sound Automatic speech recognition (ASR) systems struggle with domain-specific named entities, especially homophones. Contextual ASR improves recognition but often fails to capture fine-grained phoneme variations due to limited entity diversity. Moreover, prior methods treat entities as independent tokens, leading to incomplete multi-token biasing. To address these issues, we propose Phoneme-Augmented Robust Contextual ASR via COntrastive entity disambiguation (PARCO), which integrates phoneme-aware encoding, contrastive entity disambiguation, entity-level supervision, and hierarchical entity filtering. These components enhance phonetic discrimination, ensure complete entity retrieval, and reduce false positives under uncertainty. Experiments show that PARCO achieves CER of 4.22% on Chinese AISHELL-1 and WER of 11.14% on English DATA2 under 1,000 distractors, significantly outperforming baselines. PARCO also demonstrates robust gains on out-of-domain datasets like THCHS-30 and LibriSpeech. |
| title | PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation |
| topic | Computation and Language Artificial Intelligence Machine Learning Sound |
| url | https://arxiv.org/abs/2509.04357 |