Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions

时间 2021-01-18

标签语音识别asr 深度学习繁體版

原文原文链接

1.论文摘要提出了一种time-depth separable 的卷积网络结构，作为ED模型的encoder,在显著减少了参数量的同时增加了计算速度，并且可以维持较大的感受野范围，在noisy LibriSpeech test set 取得了WER 22%的提升。 2.模型结构 encoder TDS 的卷积结构，采用了一个2d卷积，这里输入维度为（batch_size, 1, time_ste

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1. 00036-Xception:Deep Learning with Depthwise Separable Convolutions
2. 翻译Xception:Deep Learning with Depthwise Separable Convolutions
3. Inception系列4_Xception： Deep Learning with Depthwise Separable Convolutions
4. Food Log with Speech Recognition and NLP
5. (7) [IEEEAcoustic13] SPEECH RECOGNITION WITH DEEP RECURRENT NEURAL NETWORKS
6. 文章：Emotion Recognition From Speech With Recurrent Neural Networks
7. TRANSFORMER-TRANSDUCER:END-TO-END SPEECH RECOGNITION WITH SELF-ATTENTION
8. Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody
9. speech recognition resource
10. Xception：Deep Learning with Depthwise Separable Convolutions论文阅读解释

>>更多相关文章<<