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首页> 外文会议>International Symposium on Chinese Spoken Language Processing >A speaker-dependent deep learning approach to joint speech separation and acoustic modeling for multi-talker automatic speech recognition
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A speaker-dependent deep learning approach to joint speech separation and acoustic modeling for multi-talker automatic speech recognition

机译：一种扬声器依赖性深入学习方法，用于多讲车自动语音识别的联合语音分离和声学建模

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摘要

We propose a novel speaker-dependent (SD) approach to joint training of deep neural networks (DNNs) with an explicit speech separation structure for multi-talker speech recognition in a single-channel setting. First, a multi-condition training strategy is designed for a SD-DNN recognizer in multi-talker scenarios, which can significantly reduce the decoding runtime and improve the recognition accuracy over the approaches that use speaker-independent DNN models with a complicated joint decoding framework. In addition, a SD regression DNN for mapping the acoustic features of mixed speech to the speech features of a target speaker is jointly trained with the SD recognition DNN for acoustic modeling. Our experiments on the Speech Separation Challenge (SSC) task show that the proposed SD recognition system under multi-condition training achieves an average word error rate (WER) of 3.8%, yielding a relative WER reduction of 65.1% from the proposed DNN preprocessing approach under clean-condition training [1]. Furthermore, the jointly trained DNN system generates a relative WER reduction of 13.2% from the state-of-the-art systems under multi-condition training.
机译：我们提出了一种新颖的扬声器依赖性（SD）方法来联合培训深神经网络（DNN），具有在单通道设置中的多讲车语音识别的明确语音分离结构。首先，多条件培训策略专为多讲话者方案中的SD-DNN识别器而设计，可以显着降低解码运行时，并通过使用具有复杂的联合解码框架的扬声器 - 独立的DNN模型来提高识别准确性。另外，用于将混合语音的声学特征映射到目标扬声器的语音特征的SD回归DNN与用于声学建模的SD识别DNN共同训练。我们对语音分离挑战（SSC）任务的实验表明，在多条件培训下，拟议的SD识别系统实现了3.8％的平均字错误率（WER），从提出的DNN预处理方法产生65.1％的相对萎缩率为65.1％在清洁条件培训下[1]。此外，在多条件训练下，共同训练的DNN系统从最先进的系统产生了13.2％的相对萎缩。

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