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EmphAssess : a Prosodic Benchmark on Assessing Emphasis Transfer in Speech-to-Speech Models

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arxiv 2312.14069 v2 pith:Z4L3EAIM submitted 2023-12-21 cs.CL cs.SDeess.AS

Maureen de Seyssel , Antony D'Avirro , Adina Williams , Emmanuel Dupoux This is my paper · ORCID
classification cs.CLcs.SDeess.AS
keywords emphasisbenchmarkprosodicspeech-to-speechemphassessencodeintroducemodel
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ProsodyLM: Uncovering the Emerging Prosody Processing Capabilities in Speech Language Models

    cs.CL 2025-07 conditional novelty 7.0 of 10

    A speech language model trained only on audiobooks with explicit word-level prosody tokens displays emerging abilities in prosody-controlled generation, emphasis and emotion understanding, and prosodic consistency acr...

  2. WHISTRESS: Enriching Transcriptions with Sentence Stress Detection

    cs.CL 2025-05 conditional novelty 6.0 of 10

    WHISTRESS extends Whisper with a token-level stress classifier trained on a new synthetic dataset, and shows zero-shot transfer to natural speech benchmarks.

  3. Segmentation-Variant Codebooks for Preservation of Paralinguistic and Prosodic Information

    eess.AS 2025-05 conditional novelty 6.0 of 10

    Quantizing speech at frame, phone, word, and utterance levels preserves emotion and prominence better than frame-only discrete units at similar bitrates.

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