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首页> 外文期刊>Consumer Electronics, IEEE Transactions on >Channel mapping using bidirectional long short-term memory for dereverberation in hands-free voice controlled devices
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Channel mapping using bidirectional long short-term memory for dereverberation in hands-free voice controlled devices

机译：在免提语音控制设备中使用双向长短期记忆进行混响的通道映射

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摘要

In this article, the reverberation problem for hands-free voice controlled devices is addressed by employing Bidirectional Long Short-Term Memory (BLSTM) recurrent neural networks. Such networks use memory blocks in the hidden units, enabling them to exploit a self-learnt amount of temporal context. The main objective of this technique is to minimize the mismatch between the distant talk (reverberant/distorted) speech and the close talk (clean) speech. To achieve this, the network is trained by mapping the cepstral feature space from the distant talk channel to its counterpart from the close talk channel frame-wisely in terms of regression. The method has been successfully evaluated on a realistically recorded reverberant French corpus by a large scale of experiments of comparing a variety of network architectures, investigating different network training targets (differential or absolute), and combining with common adaptation techniques. In addition, the robustness of this technique is also accessed by cross-room evaluation on both, a simulated French corpus and a realistic English corpus. Experimental results show that the proposed novel BLSTM dereverberation models trained by the differential targets reduce the word error rate (WER) by 16% relatively on the French corpus (intra room scenario) as well as 8% relatively on the English corpus (inter room scenario).
机译：在本文中，通过使用双向长短期记忆（BLSTM）递归神经网络解决了免提语音控制设备的混响问题。这样的网络在隐藏单元中使用存储块，从而使它们能够利用自学习量的时间上下文。该技术的主要目的是使远距离讲话（混响/失真）语音和近距离讲话（干净）语音之间的不匹配最小化。为了实现这一点，通过在回归方面将帧的特征空间从远距离通话通道映射到从近距离通话通道到对应通话通道的对应空间，来训练网络。通过比较各种网络体系结构，研究不同的网络训练目标（差分或绝对）以及与常见的适应技术相结合的大规模实验，已成功地在真实记录的混响法语语料库上对该方法进行了评估。此外，还可以通过对模拟的法国语料库和现实的英语语料库进行跨房间评估来获得该技术的鲁棒性。实验结果表明，所提出的新颖的由差分目标训练的BLSTM去混响模型相对于法语语料库（房间内场景）而言，将误码率（WER）降低了16％，而相对于英语语料库（房间间场景）而言，则将8％降低了）。

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