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Human parity benchmark for the Hub5'00 Evaluation #56

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Human parity benchmark for the Hub5'00 Evaluation #56

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Expand Up @@ -57,6 +57,7 @@ WER are we? An attempt at tracking states of the art(s) and recent results on sp

| WER (SWB) | WER (CH) | Paper | Published | Notes |
| :------- | :------- | :------------- | :-------- | :-----: |
| 5.9% | 11.3% | *Humans* [Achieving Human Parity in Conversational Speech Recognition](https://arxiv.org/abs/1610.05256) | October 2016 | *Humans* |
| 4.9% | 9.5% | [An investigation of phone-based subword units for end-to-end speech recognition](https://arxiv.org/abs/2004.04290) | April 2020 | 2 CNN + 24 layers Transformer encoder and 12 layers Transformer decoder model with char BPE and phoneme BPE units. |
| 5.0% | 9.1% | [The CAPIO 2017 Conversational Speech Recognition System](https://arxiv.org/abs/1801.00059) | December 2017 | 2 Dense LSTMs + 3 CNN-bLSTMs across 3 phonesets from [previous Capio paper](https://pdfs.semanticscholar.org/d0ec/cd60d800308cd6e59810769b92b40961c09a.pdf) & AM adaptation using parameter averaging (5.6% SWB / 10.5% CH single systems) |
| 5.1% | 9.9% | [Language Modeling with Highway LSTM](https://arxiv.org/abs/1709.06436) | September 2017 | HW-LSTM LM trained with Switchboard+Fisher+Gigaword+Broadcast News+Conversations, AM from [previous IBM paper](https://arxiv.org/abs/1703.02136)|
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