Introduction

Naylor, Patrick A.; Gaubitch, Nikolay D.

doi:10.1007/978-1-84996-056-4_1

Cited by 36 publications

(54 citation statements)

References 63 publications

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“…In recent years, however, research on reverberant speech processing has achieved significant progress in both the audio processing and ASR fields [4,5], mainly driven by multidisciplinary approaches that combine ideas from room acoustics, optimal filtering, machine learning, speech modeling, enhancement, and recognition. These novel techniques are now ready to be evaluated for real-world speech enhancement and speech recognition applications.…”

Section: Y(t) = H(t) * S(t) + N(t)mentioning

confidence: 99%

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A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research

Kinoshita

Delcroix

Gannot

et al. 2016

EURASIP J. Adv. Signal Process.

314

220

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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 (ASR) techniques that are robust to reverberation. In this paper, we describe the REVERB challenge, which is an evaluation campaign that was designed to evaluate such speech enhancement (SE) and ASR techniques to reveal the state-of-the-art techniques and obtain new insights regarding potential future research directions. Even though most existing benchmark tasks and challenges for distant speech processing focus on the noise robustness issue and sometimes only on a single-channel scenario, a particular novelty of the REVERB challenge is that it is carefully designed to test robustness against reverberation, based on both real, single-channel, and multichannel recordings. This challenge attracted 27 papers, which represent 25 systems specifically designed for SE purposes and 49 systems specifically designed for ASR purposes. This paper describes the problems dealt within the challenge, provides an overview of the submitted systems, and scrutinizes them to clarify what current processing strategies appear effective in reverberant speech processing.

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Section: Y(t) = H(t) * S(t) + N(t)mentioning

confidence: 99%

“…3 International Audio Laboratories Erlangen, Erlangen, Germany. 4 University of Paderborn, Paderborn, Germany. 5 Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen-Nuremberg, Germany.…”

Section: Acknowledgementsmentioning

confidence: 99%

A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research

Kinoshita

Delcroix

Gannot

et al. 2016

EURASIP J. Adv. Signal Process.

314

220

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“…As a first measure, the system mismatch [21] is utilized, which describes how well the estimated HRIRs match the true ones. It is defined as …”

Section: A Experimental Setup and Evaluation Measuresmentioning

confidence: 99%

Retrieval of individualized head-related transfer functions for hearing aid applications

Buerger

Meier

Hofmann

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-The capability of modern hearing aids to provide hearing-impaired humans with enhanced signals, which ultimately leads to an increased speech intelligibility, may benefit from fitting the device for each subject individually. This ideally also involves the exploitation of Head-Related Impulse Responses (HRIRs). However, HRIRs vary from person to person and thus require tedious measurements for each individual. In this work, we investigate two approaches which aim at speeding up the HRIR acquisition procedure. These are continuous measurements and interpolation, where Dynamic Time Warping (DTW) as well as linear interpolation of the magnitude and phase responses are considered. In contrast to related publications, the continuous HRIR measurements are not performed in anechoic environments here. The quality of the obtained HRIRs is assessed by means of the system mismatch and the proposed error of relative transfer functions. Both measures reveal that continuous HRIR measurements are on average much more capable than the investigated interpolation approaches, and they furthermore provide a more uniform performance for different source directions.

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“…The convolutive nature of reverberation induces a long-term correlation between a current observation and past observations of reverberant speech. This longterm correlation has been exploited to mitigate the effect of reverberation directly on the speech signal (i.e., speech [9][10][11][12] or feature [13,14] dereverberation) or on the acoustic model used for recognition [15,16]. The REVERB challenge [17] was organized to evaluate recent progress in the field of reverberant speech enhancement (SE) and recognition.…”

Section: Y(t) = H(t) * S(t) + N(t)mentioning

confidence: 99%

Strategies for distant speech recognitionin reverberant environments

Delcroix

Yoshioka

Ogawa

et al. 2015

EURASIP J. Adv. Signal Process.

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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 realize high-performance hands-free speech recognition. In this paper, we review a recognition system that we developed at our laboratory to deal with reverberant speech. The system consists of a speech enhancement (SE) front-end that employs long-term linear prediction-based dereverberation followed by noise reduction. We combine our SE front-end with an ASR back-end that uses neural networks for acoustic and language modeling. The proposed system achieved top scores on the ASR task of the REVERB challenge. This paper describes the different technologies used in our system and presents detailed experimental results that justify our implementation choices and may provide hints for designing distant ASR systems.

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Introduction

Cited by 36 publications

References 63 publications

A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research

A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research

Retrieval of individualized head-related transfer functions for hearing aid applications

Strategies for distant speech recognitionin reverberant environments

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