“智能硬件-课程阅读1”版本间的差异
来自iCenter Wiki
| 第1行： | 第1行： | ||
| − | ===Group1=== | + | === Group1 === |
| − | + | ||
| − | + | Google | |
| − | : | + | : Convolutional, long short-term memory, fully connected deep neural networks, ICASSP 2015. |
| − | ===Group2=== | + | : Context dependent phone models for LSTM RNN acoustic modelling, ICASSP 2015. |
| + | |||
| + | === Group2 === | ||
Google | Google | ||
| − | :Listen, attend and spell: A neural network for large vocabulary conversational speech recognition, ICASSP 2015. | + | : Listen, attend and spell: A neural network for large vocabulary conversational speech recognition, ICASSP 2015. |
| + | |||
| + | === Group3 === | ||
| − | |||
JHU | JHU | ||
| − | :Audio augmentation for speech recognition, InterSpeech 2015. | + | : Audio augmentation for speech recognition, InterSpeech 2015. |
| − | ===Group4=== | + | === Group4 === |
| − | Baidu | + | Baidu |
| − | :Deep Speech 2 End-to-End Speech Recognition in English and Mandarin, JMLR 2016. | + | : Deep Speech 2 End-to-End Speech Recognition in English and Mandarin, JMLR 2016. |
2017年1月24日 (二) 18:45的最后版本
Group1
- Convolutional, long short-term memory, fully connected deep neural networks, ICASSP 2015.
- Context dependent phone models for LSTM RNN acoustic modelling, ICASSP 2015.
Group2
- Listen, attend and spell: A neural network for large vocabulary conversational speech recognition, ICASSP 2015.
Group3
JHU
- Audio augmentation for speech recognition, InterSpeech 2015.
Group4
Baidu
- Deep Speech 2 End-to-End Speech Recognition in English and Mandarin, JMLR 2016.
