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- W4309353705 abstract "In today's world, automatic speech recognition (ASR) system-based applications are in boosting demand. The ASR has to be robust against real world noise and acoustic distorting conditions. In this chapter, we pivot on the challenges faced by ASR, the cocktail party problem and elimination of adjoining noise. The speech input to ASR should be of good quality and intelligibility. Also, it should be trained to identify the central speaker in a multi-speaker environment as stated at the cocktail party problem. The chapter deals with the techniques used in the suppression of real-world noise and speaker separation. In both single-channel speech enhancement and multi-speaker separation, well-trained deep neural network (DNN) models can be used. Mask-approximation and signal-approximation techniques can be used to train DNN for enhancement problems. Deep clustering is addressed to train DNN for speaker separation." @default.
- W4309353705 created "2022-11-26" @default.
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- W4309353705 date "2022-11-18" @default.
- W4309353705 modified "2023-09-26" @default.
- W4309353705 title "<scp>DNN</scp> Based Speech Quality Enhancement and Multi‐speaker Separation for Automatic Speech Recognition System" @default.
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- W4309353705 doi "https://doi.org/10.1002/9781119861850.ch13" @default.
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