Non-intrusive speech-quality assessment using vocal-tract models

Gray, Peter J.; Hollier, M. P.; Massara, R.E.

doi:10.1049/ip-vis:20000539

Cited by 67 publications

(30 citation statements)

References 8 publications

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“…As reported in [47], the average correlation between the MOS_LQS and MOS_LQO achieved by the P.563, including those for the 24 known MOS test databases used in the development of the model, is about 0.88. This is compared to a correlation of 0.93 achieved by the PESQ for the same task [43].…”

Section: P563 Performance and Target Applicationsmentioning

confidence: 99%

“…In 2000, Gray et al [43] reported a novel use of the vocal-tract modelling technique, which enables the prediction of the quality of a network degraded speech stream to be made in a nonintrusive way. However, although good results were reported, the technique suffers from the following drawbacks: (a) its performance seems to be affected by the gender of the speaker, (b) its application is limited to speech signals with a relatively short duration in time, (c) its performance is influenced by distorted signals with a constant level of distortions, and (d) the vocal-tract parameters are only meaningful when they are extracted from a speech stream that is the result of glottal excitation illuminating an open tract.…”

Section: A Priori Based Approachmentioning

confidence: 99%

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Advances in voice quality measurement in modern telecommunications

Mahdi

Picovici

2009

Digital Signal Processing

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Section: P563 Performance and Target Applicationsmentioning

confidence: 99%

Section: A Priori Based Approachmentioning

confidence: 99%

Advances in voice quality measurement in modern telecommunications

Mahdi

Picovici

2009

Digital Signal Processing

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“…Over the last few years, a number of non-intrusive measures have been proposed [13][14][15][16]. Recently, the ITU-T released Recommendation P.563 as its standard algorithm for a single-ended (no reference) speech quality assessment for narrow-band telephony applications [17,18,4].…”

Section: Introductionmentioning

confidence: 99%

New single-ended objective measure for non-intrusive speech quality evaluation

Mahdi

Picovici

2008

SIViP

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This article proposes a new output-based method for non-intrusive assessment of speech quality of voice communication systems and evaluates its performance. The method requires access to the processed (degraded) speech only, and is based on measuring perception-motivated objective auditory distances between the voiced parts of the output speech to appropriately matching references extracted from a pre-formulated codebook. The codebook is formed by optimally clustering a large number of parametric speech vectors extracted from a database of clean speech records. The auditory distances are then mapped into objective Mean Opinion listening quality scores. An efficient data-mining tool known as the Self-Organizing Map (SOM) achieves the required clustering and mapping/reference matching processes. In order to obtain a perception-based, speaker-independent parametric representation of the speech, three domain transformation techniques have been investigated. The first technique is based on a Perceptual Linear Prediction (PLP) model, the second utilises a Bark Spectrum (BS) analysis and the third utilises Mel-Frequency Cepstrum Coefficients (MFCC). Reported evaluation results show that the proposed method provides high correlation with subjective listening quality scores, yielding accuracy similar to that of the ITU-T P.563 while maintaining a relatively low computational complexity. Results also demonstrate that the method outperforms the PESQ in a number of distortion conditions, such as those of speech degraded by channel impairments.

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“…Nonintrusive measurement depends only on the test speech signal and is more challenging to the objective speech quality estimation. Nonintrusive models have been proposed in [15]- [17], but only recently has the ITU-T released P.563 as its nonintrusive objective quality measurement standard algorithm [18]. In speech separation applications, an intrusive approach may be inapplicable because the reference speech signals are unavailable, so the nonintrusive method is recommended.…”

Section: Introductionmentioning

confidence: 99%

Monaural Speech Separation Based on Computational Auditory Scene Analysis and Objective Quality Assessment of Speech

Guan

et al. 2006

IEEE Trans. Audio Speech Lang. Process.

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Abstract-Monaural speech separation is a very challenging problem in speech signal processing. It has been studied extensively, and many separation systems based on computational auditory scene analysis (CASA) have been proposed in the last two decades. Although the research on CASA has tended to introduce high-level knowledge into separation processes using primitive data-driven methods, the knowledge on speech quality still has not been combined with it. This makes the performance evaluation of CASA mainly focused on the signal-to-noise ratio (SNR) improvement. Actually, the quality of the separated speech is not directly related to its SNR. In order to solve this problem, we propose a new method which combines CASA with objective quality assessment of speech (OQAS). In the grouping process of CASA, we use OQAS as the guide to instruct the CASA system. With this combination, the performance of the speech separation can be improved not only in SNR, but also in mean opinion score (MOS). Our system is systematically evaluated and compared with previous systems, and it yields substantially better performance, especially for the subjective perceptual quality of separated speech.Index Terms-Computational auditory scene analysis (CASA), grouping, monaural speech separation, objective quality assessment of speech (OQAS), segmentation.

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Non-intrusive speech-quality assessment using vocal-tract models

Cited by 67 publications

References 8 publications

Advances in voice quality measurement in modern telecommunications

Advances in voice quality measurement in modern telecommunications

New single-ended objective measure for non-intrusive speech quality evaluation

Monaural Speech Separation Based on Computational Auditory Scene Analysis and Objective Quality Assessment of Speech

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