Speech Recognition On Switchboard Hub500
평가 지표
Percentage error
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
모델 이름 | Percentage error |
---|---|
모델 1 | 11 |
모델 2 | 12.9 |
모델 3 | 12.9 |
the-ibm-2015-english-conversational-telephone | 8.0 |
the-microsoft-2016-conversational-speech | 6.2 |
모델 6 | 11.5 |
모델 7 | 18.5 |
the-ibm-2016-english-conversational-telephone | 6.6 |
building-dnn-acoustic-models-for-large | 16 |
모델 10 | 12.9 |
모델 11 | 10.4 |
deep-speech-scaling-up-end-to-end-speech | 12.6 |
single-headed-attention-based-sequence-to | 4.7 |
building-dnn-acoustic-models-for-large | 15 |
achieving-human-parity-in-conversational | 6.6 |
the-microsoft-2016-conversational-speech | 6.3 |
on-the-limit-of-english-conversational-speech | 4.3 |
english-conversational-telephone-speech | 5.5 |
모델 19 | 12.6 |
the-microsoft-2016-conversational-speech | 6.9 |
모델 21 | 8.5 |
deep-speech-scaling-up-end-to-end-speech | 12.6 |
모델 23 | 9.2 |
the-ibm-2016-english-conversational-telephone | 6.9 |
모델 25 | 12.9 |
모델 26 | 16.1 |
deep-speech-scaling-up-end-to-end-speech | 20 |
achieving-human-parity-in-conversational | 5.8 |
모델 29 | 12.6 |
very-deep-multilingual-convolutional-neural | 12.2 |