CALM: Joint Contextual Acoustic-Linguistic Modeling for Personalization of Multi-Speaker ASR
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866916008193687552 |
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| author | Shakeel, Muhammad Fukumoto, Yosuke Maeda, Chikara Lin, Chyi-Jiunn Watanabe, Shinji |
| author_facet | Shakeel, Muhammad Fukumoto, Yosuke Maeda, Chikara Lin, Chyi-Jiunn Watanabe, Shinji |
| contents | We present CALM, a joint Contextual Acoustic-Linguistic Modeling framework for multi-speaker automatic speech recognition (ASR). In personalized AI scenarios, the joint availability of acoustic and linguistic cues naturally motivates the integration of target-speaker conditioning with contextual biasing in overlapping conversations. CALM implements this integration in an end-to-end framework through speaker embedding-driven target-speaker extraction and dynamic vocabulary-based contextual biasing. We evaluate CALM on simulated English (LibriSpeechMix) and Japanese (Corpus of Spontaneous Japanese mixtures, CSJMix). On two-speaker mixtures, CALM reduces biased word error rate (B-WER) from 12.7 to 4.7 on LibriSpeech2Mix and biased character error rate (B-CER) from 16.6 to 8.4 on CSJMix2 (eval3), demonstrating the effectiveness of joint acoustic-linguistic modeling across languages. We additionally report results on the AMI corpus (IHM-mix condition) to validate performance on standardized speech mixtures. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_22792 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CALM: Joint Contextual Acoustic-Linguistic Modeling for Personalization of Multi-Speaker ASR Shakeel, Muhammad Fukumoto, Yosuke Maeda, Chikara Lin, Chyi-Jiunn Watanabe, Shinji Audio and Speech Processing Computation and Language Sound We present CALM, a joint Contextual Acoustic-Linguistic Modeling framework for multi-speaker automatic speech recognition (ASR). In personalized AI scenarios, the joint availability of acoustic and linguistic cues naturally motivates the integration of target-speaker conditioning with contextual biasing in overlapping conversations. CALM implements this integration in an end-to-end framework through speaker embedding-driven target-speaker extraction and dynamic vocabulary-based contextual biasing. We evaluate CALM on simulated English (LibriSpeechMix) and Japanese (Corpus of Spontaneous Japanese mixtures, CSJMix). On two-speaker mixtures, CALM reduces biased word error rate (B-WER) from 12.7 to 4.7 on LibriSpeech2Mix and biased character error rate (B-CER) from 16.6 to 8.4 on CSJMix2 (eval3), demonstrating the effectiveness of joint acoustic-linguistic modeling across languages. We additionally report results on the AMI corpus (IHM-mix condition) to validate performance on standardized speech mixtures. |
| title | CALM: Joint Contextual Acoustic-Linguistic Modeling for Personalization of Multi-Speaker ASR |
| topic | Audio and Speech Processing Computation and Language Sound |
| url | https://arxiv.org/abs/2601.22792 |