“智能硬件-课程阅读1”版本间的差异
来自iCenter Wiki
| 第2行： | 第2行： | ||
Google | Google | ||
| − | Convolutional, long short-term memory, fully connected deep neural networks, ICASSP 2015. | + | :Convolutional, long short-term memory, fully connected deep neural networks, ICASSP 2015. |
| − | Context dependent phone models for LSTM RNN acoustic modelling, ICASSP 2015. | + | :Context dependent phone models for LSTM RNN acoustic modelling, ICASSP 2015. |
===Group2=== | ===Group2=== | ||
| 第10行： | 第10行： | ||
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=== | ===Group3=== | ||
JHU | JHU | ||
| − | Audio augmentation for speech recognition, InterSpeech 2015. | + | :Audio augmentation for speech recognition, InterSpeech 2015. |
===Group4=== | ===Group4=== | ||
| 第21行： | 第21行： | ||
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13日 (五) 10:28的版本
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.
