# In an open TTS evaluation, what are WER and speaker-similarity (SIM) computed with, and why can a lower WER mean worse speaker similarity?

- id: art_01M5NP7VHGF22WK12KD3WMXQVA
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T13, T20 · tags: none
- updated: 2026-10-24T00:51:13.579Z
- url: https://veyrul.com/articles/art_01M5NP7VHGF22WK12KD3WMXQVA

Problem: judge a TTS or voice-cloning output with objective numbers. Conditions: zero-shot TTS evaluated on a fixed test set. Method: the Seed-TTS evaluation (repo and paper) computes WER by transcribing the generated audio with an ASR (Whisper-large-v3 for English, Paraformer-zh for Mandarin) and comparing with the intended text, and SIM as the cosine similarity between speaker embeddings from a WavLM-large model fine-tuned for speaker verification, comparing each synthesized utterance with its reference clip. The released set uses 1,000 Common Voice samples (English) and 2,000 DiDiSpeech-2 samples (Mandarin); scripts are cal_wer.sh and cal_sim.sh. The paper cautions that a lower WER does not necessarily mean better subjective speaker similarity: lower WER often means more 'standardized', less accented or less expressive speech that the ASR finds easy. Use (inference): report WER and SIM together with a listening test; name the ASR and embedding models and versions, because scores ...

Problem: judge a TTS or voice-cloning output with objective numbers. Conditions: zero-shot TTS evaluated on a fixed test set. Method: the Seed-TTS evaluation (repo and paper) computes WER by transcribing the generated audio with an ASR (Whisper-large-v3 for English, Paraformer-zh for Mandarin) and comparing with the intended text, and SIM as the cosine similarity between speaker embeddings from a WavLM-large model fine-tuned for speaker verification, comparing each synthesized utterance with its reference clip. The released set uses 1,000 Common Voice samples (English) and 2,000 DiDiSpeech-2 samples (Mandarin); scripts are cal_wer.sh and cal_sim.sh. The paper cautions that a lower WER does not necessarily mean better subjective speaker similarity: lower WER often means more 'standardized', less accented or less expressive speech that the ASR finds easy. Use (inference): report WER and SIM together with a listening test; name the ASR and embedding models and versions, because scores shift with them; do not compare numbers across papers that use different ASRs or text normalisers. Failure modes: optimising to the ASR; embedding model bias for accents or languages. Limitations: the numbers measure intelligibility and embedding closeness, not naturalness or direction/performance; the repo does not release the Seed-TTS model itself.

Curator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.

Limitations: The paper is from the developer of the evaluated model; its claims about why WER and SIM diverge are empirical observations of the authors. Cross-paper comparability is my inference.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://arxiv.org/pdf/2406.02430",
      "note": "Evaluation metrics; Evaluation metrics; In-context learning results; Metrics"
    },
    {
      "url": "https://raw.githubusercontent.com/BytedanceSpeech/seed-tts-eval/main/README.md",
      "note": "Evaluation metrics; Evaluation metrics; In-context learning results; Metrics"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] Problem: judge a TTS or voice-cloning output with objective numbers. Conditions: zero-shot TTS evaluated on a fixed test set. Method: the Seed-TTS evaluation (repo and paper) computes WER by transcribing the generated audio with an ASR (Whisper-large-v3 for English, Paraformer-zh for Mandarin) and comparing with the intended text, and SIM as the cosine similarity between speaker embeddings from a WavLM-large model fine-tuned for speaker verification, comparing each synthesized utterance with its reference clip. The released set uses 1,000 Common Voice samples (English) and 2,000 DiDiSpeech-2 samples (Mandarin); scripts are cal_wer.sh and cal_sim.sh. The paper cautions that a lower WER does not necessarily mean better subjective speaker similarity: lower WER often means more 'standardized', less accented or less expressive speech that the ASR finds easy. Use (inference): report WER and SIM together with a listening test; name the ASR and embedding models and versions, because scores shift with them; do not compare numbers across papers that use different ASRs or text normalisers. Failure modes: optimising to the ASR; embedding model bias for accents or languages. Limitations: the numbers measure intelligibility and embedding closeness, not naturalness or direction/performance; the repo does not release the Seed-TTS model itself. · locator: Evaluation metrics; Evaluation metrics; In-context learning results; Metrics · source: https://arxiv.org/pdf/2406.02430

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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