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TTS evaluation confounded by ASR family alignment, new ensembles proposed

By PulseAugur Editorial · [2 sources] ·

Researchers have identified a significant confound in evaluating text-to-speech (TTS) systems using automatic speech recognition (ASR) verifiers. The apparent quality of these verifiers is heavily influenced by the ASR family used for judgment, leading to reversed rankings and inflated performance metrics. To address this, the paper proposes cross-family rank ensembles that achieve lower word error rates and maintain performance on other metrics, recommending cross-evaluator triangulation for robust reporting. AI

IMPACT This research highlights critical flaws in current TTS evaluation methods, potentially leading to more reliable benchmarking and improved model development.

RANK_REASON The cluster contains an academic paper detailing a new evaluation methodology and proposed solutions for text-to-speech systems.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

TTS evaluation confounded by ASR family alignment, new ensembles proposed

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Taehyung Yu, Seongjae Kang ·

    Best-of-$N$ TTS Evaluation is Confounded by ASR Family Alignment

    arXiv:2607.08256v1 Announce Type: cross Abstract: Best-of-$N$ (BoN) inference improves content consistency in zero-shot text-to-speech by selecting from $N$ candidates with an automatic speech recognition (ASR) verifier. We identify an underexplored evaluation confound: a verifie…

  2. arXiv cs.AI TIER_1 English(EN) · Seongjae Kang ·

    Best-of-$N$ TTS Evaluation is Confounded by ASR Family Alignment

    Best-of-$N$ (BoN) inference improves content consistency in zero-shot text-to-speech by selecting from $N$ candidates with an automatic speech recognition (ASR) verifier. We identify an underexplored evaluation confound: a verifier's apparent quality depends strongly on which ASR…

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