A study on speech quality and speech intelligibility measures for quality assessment of single-channel dereverberation algorithms

Goetze, Stefan; Warzybok, Anna; Kodrasi, Ina; Jungmann, Jan Ole; Cauchi, Benjamin; Rennies, Jan; Habets, Emanuël A. P.; Mertins, Alfred; Gerkmann, Timo; Doclo, Simon; Kollmeier, Birger

doi:10.1109/iwaenc.2014.6954293

Cited by 17 publications

(15 citation statements)

References 13 publications

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“…As expected, the selected instrumental measures do not always show completely consistent results [37,38]. Nevertheless, some common tendencies can clearly be observed, which will be summarized next.…”

Section: Instrumental Speech Quality Measuresmentioning

confidence: 59%

“…Since various instrumental speech quality measures exist which can be used to assess the quality of denoised and dereverberated signals [37][38][39] and since it is difficult to assess the quality using only one single measure, the performance of the proposed system has been evaluated using the five signal-based quality measures suggested in [26], i.e., the speech to reverberation modulation energy ratio (SRMR) [40], the cepstral distance (CD) [41], the log likelihood ratio (LLR) [41], the frequency-weighted segmental SNR (FWSSNR) [41], and the perceptual evaluation of speech quality (PESQ) [42].…”

Section: Instrumental Speech Quality Measuresmentioning

confidence: 99%

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Combination of MVDR beamforming and single-channel spectral processing for enhancing noisy and reverberant speech

Cauchi

Kodrasi

Rehr

et al. 2015

EURASIP J. Adv. Signal Process.

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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 distortionless response beamformer with an online estimated noise coherence matrix is used to suppress noise and reverberation. The output of this beamformer is then processed by a single-channel spectral enhancement scheme, based on statistical room acoustics, minimum statistics, and temporal cepstrum smoothing, to suppress residual noise and reverberation. The evaluation is conducted using the REVERB challenge corpus, designed to evaluate speech enhancement algorithms in the presence of both reverberation and noise. The proposed system is evaluated using instrumental speech quality measures, the performance of an automatic speech recognition system, and a subjective evaluation of the speech quality based on a MUSHRA test. The performance achieved by beamforming, single-channel spectral enhancement, and their combination are compared, and experimental results show that the proposed system is effective in suppressing both reverberation and noise while improving the speech quality. The achieved improvements are particularly significant in conditions with high reverberation times

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Section: Instrumental Speech Quality Measuresmentioning

confidence: 59%

Section: Instrumental Speech Quality Measuresmentioning

confidence: 99%

Combination of MVDR beamforming and single-channel spectral processing for enhancing noisy and reverberant speech

Cauchi

Kodrasi

Rehr

et al. 2015

EURASIP J. Adv. Signal Process.

Self Cite

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show abstract

“…For the computation of the recognition rate, only the letter and the number in the utterance are evaluated, as in the CHiME challenge [45]. Furthermore, two signal-based measures for the overall speech quality are evaluated, which were shown to be significantly correlated to the perceived amount of reverberation [46]: PESQ [47] and the frequency-weighted segmental signal-to-noise ratio (fwSegSNR) [41]. We use the wideband version of PESQ and give values in the MOS-LQO scale.…”

Section: Measures and Evaluation Methodmentioning

confidence: 99%

Coherent-to-Diffuse Power Ratio Estimation for Dereverberation

Schwarz

Kellermann

2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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The estimation of the time- and frequency-dependent coherent-to-diffuse power ratio (CDR) from the measured spatial coherence between two omnidirectional microphones is investigated. Known CDR estimators are formulated in a common framework, illustrated using a geometric interpretation in the complex plane, and investigated with respect to bias and robustness towards model errors. Several novel unbiased CDR estimators are proposed, and it is shown that knowledge of either the direction of arrival (DOA) of the target source or the coherence of the noise field is sufficient for unbiased CDR estimation. The validity of the model for the application of CDR estimates to dereverberation is investigated using measured and simulated impulse responses. A CDR-based dereverberation system is presented and evaluated using signal-based quality measures as well as automatic speech recognition accuracy. The results show that the proposed unbiased estimators have a practical advantage over existing estimators, and that the proposed DOA-independent estimator can be used for effective blind dereverberation.Comment: submitted to IEEE/ACM Transactions on Audio, Speech, and Language Processing, 201

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“…A higher value for PESQ and a lower value for CD and RDT indicate a better performance. These measures exhibit a high correlation with subjective listening tests evaluating the quality of the dereverberated speech signals [38]. The improvements obtained in PESQ, denoted by ∆PESQ, and the reductions obtained in CD and RDT, denoted by ∆CD and ∆RDT, respectively, for the considered dereverberation methods are shown in the subsequent sections for better readability.…”

Section: A Evaluation Setup and Implementation Detailsmentioning

confidence: 85%

Speech Dereverberation Using Non-Negative Convolutive Transfer Function and Spectro-Temporal Modeling

Mohammadiha

Doclo

2016

IEEE/ACM Trans. Audio Speech Lang. Process.

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This paper presents two single-channel speech dereverberation methods to enhance the quality of speech signals that have been recorded in an enclosed space. For both methods, the room acoustics are modeled using a non-negative approximation of the convolutive transfer function (N-CTF), and to additionally exploit the spectral properties of the speech signal, such as the low-rank nature of the speech spectrogram, the speech spectrogram is modeled using non-negative matrix factorization (NMF). Two methods are described to combine the N-CTF and NMF models. In the first method, referred to as the integrated method, a cost function is constructed by directly integrating the speech NMF model into the N-CTF model, while in the second method, referred to as the weighted method, the N-CTF and NMF based cost functions are weighted and summed. Efficient update rules are derived to solve both optimization problems. In addition, an extension of the integrated method is presented, which exploits the temporal dependencies of the speech signal. Several experiments are performed on reverberant speech signals with and without background noise, where the integrated method yields a considerably higher speech quality than the baseline N-CTF method and a state-of-the-art spectral enhancement method. Moreover, the experimental results indicate that the weighted method can even lead to a better performance in terms of instrumental quality measures, but that the optimal weighting parameter depends on the room acoustics and the utilized NMF model. Modeling the temporal dependencies in the integrated method was found to be useful only for highly reverberant conditions.

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A study on speech quality and speech intelligibility measures for quality assessment of single-channel dereverberation algorithms

Cited by 17 publications

References 13 publications

Combination of MVDR beamforming and single-channel spectral processing for enhancing noisy and reverberant speech

Combination of MVDR beamforming and single-channel spectral processing for enhancing noisy and reverberant speech

Coherent-to-Diffuse Power Ratio Estimation for Dereverberation

Speech Dereverberation Using Non-Negative Convolutive Transfer Function and Spectro-Temporal Modeling

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