Speech Recognition
Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.
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Papers
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All datasetsLibriSpeech test-cleanLibriSpeech test-otherSwitchboard + Hub500TIMITAISHELL-1WSJ eval92Common Voice Germanswb_hub_500 WER fullSWBCHTUDACommon Voice FrenchCommon Voice SpanishMediaSpeech
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | AmNet | Word Error Rate (WER) | 8.6 | — | Unverified |
| 2 | HMM-(SAT)GMM | Word Error Rate (WER) | 8 | — | Unverified |
| 3 | Local Prior Matching (Large Model) | Word Error Rate (WER) | 7.19 | — | Unverified |
| 4 | Snips | Word Error Rate (WER) | 6.4 | — | Unverified |
| 5 | Li-GRU | Word Error Rate (WER) | 6.2 | — | Unverified |
| 6 | HMM-DNN + pNorm* | Word Error Rate (WER) | 5.5 | — | Unverified |
| 7 | CTC + policy learning | Word Error Rate (WER) | 5.42 | — | Unverified |
| 8 | Deep Speech 2 | Word Error Rate (WER) | 5.33 | — | Unverified |
| 9 | HMM-TDNN + iVectors | Word Error Rate (WER) | 4.8 | — | Unverified |
| 10 | Gated ConvNets | Word Error Rate (WER) | 4.8 | — | Unverified |