ASR for Affective Speech: Investigating Impact of Emotion and Speech Generative Strategy
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917227145461760 |
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| author | Wu, Ya-Tse Lee, Chi-Chun |
| author_facet | Wu, Ya-Tse Lee, Chi-Chun |
| contents | This work investigates how emotional speech and generative strategies affect ASR performance. We analyze speech synthesized from three emotional TTS models and find that substitution errors dominate, with emotional expressiveness varying across models. Based on these insights, we introduce two generative strategies: one using transcription correctness and another using emotional salience, to construct fine-tuning subsets. Results show consistent WER improvements on real emotional datasets without noticeable degradation on clean LibriSpeech utterances. The combined strategy achieves the strongest gains, particularly for expressive speech. These findings highlight the importance of targeted augmentation for building emotion-aware ASR systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20319 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ASR for Affective Speech: Investigating Impact of Emotion and Speech Generative Strategy Wu, Ya-Tse Lee, Chi-Chun Audio and Speech Processing This work investigates how emotional speech and generative strategies affect ASR performance. We analyze speech synthesized from three emotional TTS models and find that substitution errors dominate, with emotional expressiveness varying across models. Based on these insights, we introduce two generative strategies: one using transcription correctness and another using emotional salience, to construct fine-tuning subsets. Results show consistent WER improvements on real emotional datasets without noticeable degradation on clean LibriSpeech utterances. The combined strategy achieves the strongest gains, particularly for expressive speech. These findings highlight the importance of targeted augmentation for building emotion-aware ASR systems. |
| title | ASR for Affective Speech: Investigating Impact of Emotion and Speech Generative Strategy |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2601.20319 |