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'Silencing the Echoes' - Processing of Reverberant Speech

The Reverberant Voice Enhancement and Recognition Benchmark challenge has recently been organized, in order to enable researchers in the field of reverberant speech processing to carry out comprehensive evaluations of their methods based on a common database and common evaluation metrics. Inspired by the great interest generated by this challenge, we invite contributions on processing of reverberant speech signals for both signal enhancement to increase perceptual speech quality and for robust recognition of reverberant speech.

Edited by: Sharon Gannot, Armin Sehr, Emanuël Habets, Keisuke Kinoshita, Walter Kellermann and Reinhold Haeb-Umbach

  1. In recent years, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel dereverberation techniques and automatic speech recognition (AS...

    Authors: Keisuke Kinoshita, Marc Delcroix, Sharon Gannot, Emanuël A. P. Habets, Reinhold Haeb-Umbach, Walter Kellermann, Volker Leutnant, Roland Maas, Tomohiro Nakatani, Bhiksha Raj, Armin Sehr and Takuya Yoshioka
    Citation: EURASIP Journal on Advances in Signal Processing 2016 2016:7
    Content type: Research Published on:

  2. This paper investigates deep neural networks (DNN) based on nonlinear feature mapping and statistical linear feature adaptation approaches for reducing reverberation in speech signals. In the nonlinear feature...

    Authors: Xiong Xiao, Shengkui Zhao, Duc Hoang Ha Nguyen, Xionghu Zhong, Douglas L. Jones, Eng Siong Chng and Haizhou Li
    Citation: EURASIP Journal on Advances in Signal Processing 2016 2016:4
    Content type: Research Published on:

  3. In this paper, we propose an environment-dependent denoising autoencoder (DAE) and automatic environment identification based on a deep neural network (DNN) with blind reverberation estimation for robust dista...

    Authors: Yuma Ueda, Longbiao Wang, Atsuhiko Kai and Bo Ren
    Citation: EURASIP Journal on Advances in Signal Processing 2015 2015:92
    Content type: Research Published on:

  4. This paper describes a novel two-stage dereverberation feature enhancement method for noise-robust automatic speech recognition. In the first stage, an estimate of the dereverberated speech is generated by mat...

    Authors: Sami Keronen, Heikki Kallasjoki, Kalle J. Palomäki, Guy J. Brown and Jort F. Gemmeke
    Citation: EURASIP Journal on Advances in Signal Processing 2015 2015:76
    Content type: Research Published on:

  5. This paper presents extended techniques aiming at the improvement of automatic speech recognition (ASR) in single-channel scenarios in the context of the REVERB (REverberant Voice Enhancement and Recognition B...

    Authors: Feifei Xiong, Bernd T. Meyer, Niko Moritz, Robert Rehr, Jörn Anemüller, Timo Gerkmann, Simon Doclo and Stefan Goetze
    Citation: EURASIP Journal on Advances in Signal Processing 2015 2015:70
    Content type: Research Published on:

  6. We propose an approach to reverberant speech recognition adopting deep learning in the front-end as well as b a c k-e n d o f a r e v e r b e r a n t s p e e c h r e c o g n i t i o n s y s t e m, a n d a n o ...

    Authors: Masato Mimura, Shinsuke Sakai and Tatsuya Kawahara
    Citation: EURASIP Journal on Advances in Signal Processing 2015 2015:62
    Content type: Research Published on:

  7. This paper presents a system aiming at joint dereverberation and noise reduction by applying a combination of a beamformer with a single-channel spectral enhancement scheme. First, a minimum variance distortio...

    Authors: Benjamin Cauchi, Ina Kodrasi, Robert Rehr, Stephan Gerlach, Ante Jukić, Timo Gerkmann, Simon Doclo and Stefan Goetze
    Citation: EURASIP Journal on Advances in Signal Processing 2015 2015:61
    Content type: Research Published on:

  8. Reverberation and noise are known to severely affect the automatic speech recognition (ASR) performance of speech recorded by distant microphones. Therefore, we must deal with reverberation if we are to realiz...

    Authors: Marc Delcroix, Takuya Yoshioka, Atsunori Ogawa, Yotaro Kubo, Masakiyo Fujimoto, Nobutaka Ito, Keisuke Kinoshita, Miquel Espi, Shoko Araki, Takaaki Hori and Tomohiro Nakatani
    Citation: EURASIP Journal on Advances in Signal Processing 2015 2015:60
    Content type: Research Published on:

  9. We present single-channel approaches to robust automatic speech recognition (ASR) in reverberant environments based on non-intrusive estimation of the clarity index (C 50). Our best perfor...

    Authors: Pablo Peso Parada, Dushyant Sharma, Patrick A. Naylor and Toon van Waterschoot
    Citation: EURASIP Journal on Advances in Signal Processing 2015 2015:54
    Content type: Research Published on:

  10. The recently released REverberant Voice Enhancement and Recognition Benchmark (REVERB) challenge includes a reverberant automatic speech recognition (ASR) task. This paper describes our proposed system based o...

    Authors: Yuuki Tachioka, Tomohiro Narita and Shinji Watanabe
    Citation: EURASIP Journal on Advances in Signal Processing 2015 2015:52
    Content type: Research Published on:

  11. The REVERB challenge provides a common framework for the evaluation of feature extraction techniques in the presence of both reverberation and additive background noise. State-of-the-art speech recognition sys...

    Authors: Md Jahangir Alam, Vishwa Gupta, Patrick Kenny and Pierre Dumouchel
    Citation: EURASIP Journal on Advances in Signal Processing 2015 2015:50
    Content type: Research Published on: