Conversational Speech Naturalness Predictor
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915827360464896 |
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| author | Xu, Anfeng Gaur, Yashesh Kanda, Naoyuki Ouyang, Zhicheng Zmolikova, Katerina Raj, Desh Merello, Simone Sun, Anna Kalinli, Ozlem |
| author_facet | Xu, Anfeng Gaur, Yashesh Kanda, Naoyuki Ouyang, Zhicheng Zmolikova, Katerina Raj, Desh Merello, Simone Sun, Anna Kalinli, Ozlem |
| contents | Evaluation of conversational naturalness is essential for developing human-like speech agents. However, existing speech naturalness predictors are often designed to assess utterances from a single speaker, failing to capture conversation-level naturalness qualities. In this paper, we present a framework for an automatic naturalness predictor for two-speaker, multi-turn conversations. We first show that existing naturalness estimators have low, or sometimes even negative, correlations with conversational naturalness, based on conversational recordings annotated with human ratings. We then propose a dual-channel naturalness estimator, in which we investigate multiple pre-trained encoders with data augmentation. Our proposed model achieves substantially higher correlation with human judgments compared to existing naturalness predictors for both in-domain and out-of-domain conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01467 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conversational Speech Naturalness Predictor Xu, Anfeng Gaur, Yashesh Kanda, Naoyuki Ouyang, Zhicheng Zmolikova, Katerina Raj, Desh Merello, Simone Sun, Anna Kalinli, Ozlem Audio and Speech Processing Evaluation of conversational naturalness is essential for developing human-like speech agents. However, existing speech naturalness predictors are often designed to assess utterances from a single speaker, failing to capture conversation-level naturalness qualities. In this paper, we present a framework for an automatic naturalness predictor for two-speaker, multi-turn conversations. We first show that existing naturalness estimators have low, or sometimes even negative, correlations with conversational naturalness, based on conversational recordings annotated with human ratings. We then propose a dual-channel naturalness estimator, in which we investigate multiple pre-trained encoders with data augmentation. Our proposed model achieves substantially higher correlation with human judgments compared to existing naturalness predictors for both in-domain and out-of-domain conditions. |
| title | Conversational Speech Naturalness Predictor |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2603.01467 |