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Research quantifies ASR errors in TTS evaluation

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the accuracy of text-to-speech (TTS) systems by analyzing the errors introduced by automatic speech recognition (ASR) systems used in their evaluation. The study found that ASR systems miss a significant percentage of rare words, even when transcribing human speech, and that these errors can inflate the perceived error rate of TTS systems. By using multiple ASR systems, the researchers were able to reduce the apparent TTS error rate for rare words, though these words remained the most challenging to process. AI

IMPACT This research highlights the need for more robust evaluation metrics in speech synthesis, potentially leading to more accurate TTS systems.

RANK_REASON The cluster contains an academic paper published on arXiv detailing research findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Research quantifies ASR errors in TTS evaluation

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The cluster contains an academic paper published on arXiv detailing research findings. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Kirill Borodin, Vasilii Kudryavtsev, Nikita Vasiliev, Grach Mkrtchian ·

    Measuring the Verifier's Floor: How Much Apparent Rare-Word Text-to-Speech Error Is ASR Error?

    arXiv:2610.31470v1 Announce Type: cross Abstract: Text-to-speech (TTS) is usually evaluated by transcribing its output with an automatic speech recognition (ASR) system, so the word error rate also counts recognizer errors. We measure these errors on human recordings of the test …

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