符合规定
发表于 2025-3-23 09:51:11
Shinji Watanabe,Marc Delcroix,John R. HersheyField of automatic speech recognition has evolved greatly since the introduction of deep learning.Covers the state-of-the-art in noise robustness for deep neural-network-based speech recognition.Inclu
CODE
发表于 2025-3-23 14:45:39
978-3-319-87849-2Springer International Publishing AG 2017
Oversee
发表于 2025-3-23 18:12:25
Multichannel Speech Enhancement Approaches to DNN-Based Far-Field Speech Recognitionased multichannel approaches and describe beamforming-based noise reduction and linear-prediction-based dereverberation. We demonstrate the potential of these approaches by introducing two systems that achieved top performance on the recent REVERB and CHiME-3 benchmarks.
诙谐
发表于 2025-3-24 00:55:18
Distant Speech Recognition Experiments Using the AMI Corpushes using microphone array beamforming followed by single-channel acoustic modelling with approaches which combine multichannel signal processing with acoustic modelling in the context of convolutional networks.
小溪
发表于 2025-3-24 02:42:00
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先兆
发表于 2025-3-24 09:53:58
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提名
发表于 2025-3-24 10:44:18
Preliminaries background of robustness issues of deep neural-network-based ASR. It provides an overview of robust ASR research including a brief history of several studies before the deep learning era, basic formulations of ASR, signal processing, and neural networks. This chapter also introduces common notation
毗邻
发表于 2025-3-24 17:00:02
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Immunization
发表于 2025-3-24 19:20:04
Multichannel Spatial Clustering Using Model-Based Source Separation important regard, like the number and arrangement of microphones or the reverberation and noise conditions. Because these configurations are difficult to predict a priori and difficult to exhaustively train over, the use of unsupervised spatial-clustering methods is attractive. Such methods separat
狼群
发表于 2025-3-25 02:36:36
Discriminative Beamforming with Phase-Aware Neural Networks for Speech Enhancement and Recognitionlong processing pipeline, the processing steps are usually designed to optimize cost functions that are not directly related to the task, leading to suboptimal performance. In this chapter, we introduce a beamforming (BF) network to perform spatial filtering that is optimal for the ASR task. The BF