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        <title>EmphAssess : a Prosodic Benchmark on Assessing Emphasis Transfer in Speech-to-Speech Models</title>
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    <abstract>We introduce EmphAssess, a prosodic benchmark designed to evaluate the capability of speech-to-speech models to encode and reproduce prosodic emphasis. We apply this to two tasks: speech resynthesis and speech-to-speech translation. In both cases, the benchmark evaluates the ability of the model to encode emphasis in the speech input and accurately reproduce it in the output, potentially across a change of speaker and language. As part of the evaluation pipeline, we introduce EmphaClass, a new model that classifies emphasis at the frame or word level.</abstract>
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%0 Conference Proceedings
%T EmphAssess : a Prosodic Benchmark on Assessing Emphasis Transfer in Speech-to-Speech Models
%A Seyssel, Maureen
%A D’Avirro, Antony
%A Williams, Adina
%A Dupoux, Emmanuel
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F seyssel-etal-2024-emphassess
%X We introduce EmphAssess, a prosodic benchmark designed to evaluate the capability of speech-to-speech models to encode and reproduce prosodic emphasis. We apply this to two tasks: speech resynthesis and speech-to-speech translation. In both cases, the benchmark evaluates the ability of the model to encode emphasis in the speech input and accurately reproduce it in the output, potentially across a change of speaker and language. As part of the evaluation pipeline, we introduce EmphaClass, a new model that classifies emphasis at the frame or word level.
%U https://aclanthology.org/2024.emnlp-main.30
%P 495-507

Markdown (Informal)

[EmphAssess : a Prosodic Benchmark on Assessing Emphasis Transfer in Speech-to-Speech Models](https://aclanthology.org/2024.emnlp-main.30) (Seyssel et al., EMNLP 2024)

• EmphAssess : a Prosodic Benchmark on Assessing Emphasis Transfer in Speech-to-Speech Models (Seyssel et al., EMNLP 2024)

ACL

• Maureen Seyssel, Antony D’Avirro, Adina Williams, and Emmanuel Dupoux. 2024. EmphAssess : a Prosodic Benchmark on Assessing Emphasis Transfer in Speech-to-Speech Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 495–507, Miami, Florida, USA. Association for Computational Linguistics.