Inference Skipping for More Efficient Real-Time Speech Enhancement With Parallel RNNs

Deep neural network (DNN) based speech enhancement models have attracted extensive attention due to their promising performance. However, it is difficult to deploy a powerful DNN in real-time applications because of its high computational cost. Typical compression methods such as pruning and quantiz...

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Veröffentlicht in:	IEEE/ACM transactions on audio, speech, and language processing speech, and language processing, 2022, Vol.30, p.2411-2421
Hauptverfasser:	Le, Xiaohuai, Lei, Tong, Chen, Kai, Lu, Jing
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Business competition Computational modeling Computing costs Convolution Decoding Dual-path RNN Load modeling model compression Noise measurement Pruning Real time Real-time systems Recurrent neural networks Skip-RNN Speech enhancement Speech processing Strategy Voice activity detectors Voice recognition
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