If you mean freely accessible benchmark sites for testing model quality and evaluation methods around speech/voice AI, the best options depend on whether you want ASR, speech synthesis (TTS), voice conversion, or general speech understanding.

Here are the most useful public benchmark-style resources:

1) Hugging Face Datasets + Papers with Code

• Best for: discovering standard speech benchmarks and comparing SOTA results
• Why it’s useful: many speech tasks are cataloged with datasets, leaderboards, and evaluation metrics
• Good for: ASR, speaker ID, speech enhancement, TTS-related tasks
• Sites:
  • Hugging Face Datasets: https://huggingface.co/datasets
  • Papers with Code: https://paperswithcode.com/

2) Open ASR Leaderboards

• Best for: automatic speech recognition comparisons
• Common benchmarks: LibriSpeech, Common Voice, TED-LIUM, GigaSpeech
• Metrics: WER (word error rate), CER (character error rate)
• Examples:
  • Hugging Face ASR leaderboards often surface public comparisons
  • Papers with Code pages for ASR benchmarks

3) Mozilla Common Voice

• Best for: multilingual ASR benchmarking and dataset access
• Why it’s useful: large open speech dataset with many languages
• Metric: typically WER/CER
• Site: https://commonvoice.mozilla.org/

4) OpenSLR

• Best for: classic speech datasets and benchmark corpora
• Why it’s useful: widely used academic speech datasets, easy to reference
• Examples: LibriSpeech, Fisher, VoxForge, speech/noise resources
• Site: https://www.openslr.org/

5) EvalAI

• Best for: running structured benchmark challenges
• Why it’s useful: many ML competitions include speech tasks, hidden test sets, and standardized scoring
• Site: https://eval.ai/

6) Kaggle competitions / public notebooks

• Best for: practical model evaluation on speech datasets
• Why it’s useful: easy to compare approaches, kernels, and metrics
• Site: https://www.kaggle.com/competitions

7) SpeechBrain / NeMo / ESPnet benchmark examples

• Best for: evaluation methodology and reproducible baselines
• Why it’s useful: not “benchmark sites” in the leaderboard sense, but excellent for standardized evaluation pipelines
• Sites:
  • SpeechBrain: https://speechbrain.github.io/
  • NVIDIA NeMo: https://github.com/NVIDIA/NeMo
  • ESPnet: https://espnet.github.io/espnet/

8) SUPERB benchmark

• Best for: unified evaluation of speech representation models
• Why it’s useful: tests multiple speech tasks from one model representation
• Tasks: ASR, speaker ID, intent classification, emotion, etc.
• Info: often referenced via Papers with Code and associated repos

9) TTS-focused evaluation resources

If you mean speech generation, benchmarks are less centralized, but useful resources include:

• VCTK / LibriTTS / LJ Speech dataset pages
• Metrics such as:
  • MOS (mean opinion score)
  • MCD (mel cepstral distortion)
  • ASR-based intelligibility checks
• Repos from ESPnet, Coqui, and NVIDIA often include evaluation scripts

10) MLPerf Speech

• Best for: performance/throughput benchmarking rather than speech quality
• Why it’s useful: standardized inference benchmarking for deployment comparisons
• Site: https://mlcommons.org/benchmarks/

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If you want the “best” by use case

For ASR quality

• Common Voice
• LibriSpeech
• Papers with Code ASR leaderboards
• OpenSLR

For evaluation methodology

• EvalAI
• SpeechBrain / ESPnet benchmark recipes
• SUPERB

For speech generation / TTS

• LibriTTS
• LJ Speech
• MOS-style human evaluation setups
• ESPnet / Coqui evaluation scripts

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Quick recommendation

If you want a good free starting stack:

1. Papers with Code for benchmark comparisons
2. Common Voice for multilingual ASR testing
3. OpenSLR / LibriSpeech for classic ASR baselines
4. EvalAI if you want a proper hidden-test evaluation setup
5. SUPERB if you want broader speech representation evaluation

If you tell me whether you care about ASR, TTS, voice cloning, speaker recognition, or speech understanding, I can give you a tighter list of the top 5 benchmark sites and the exact metrics to use.