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Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments
Engleski
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Speech Signal Processing Based on Deep Learning in Complex Acoustic Environments provides a detailed discussion of deep learning-based robust speech processing and its applications. It begins by looking at the basics of deep learning and common deep network models, followed by front-end algorithms for deep learning-based speech denoising, speech detection, single-channel speech enhancement multi-channel speech enhancement, multi-speaker speech separation, and the applications of deep learning-based speech denoising in speaker verification and speech recognition. The book particularly emphasizes modern deep learning-based techniques for speaker verification and speech recognition, including their foundations and cutting-edge technologies. Provides a comprehensive introduction to the development of deep learning-based robust speech processing. Specifically, it covers speech detection, speech enhancement, dereverberation, multi-speaker speech separation, robust speaker verification, and robust speech recognitionFirst focuses on a historical overview, and then covers methods that demonstrate outstanding performance in practical applications, enabling readers to quickly familiarize themselves with the main technologies in this field
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