Speech Quality Estimation: Models and Trends

Möller, Sebastian; Chan, Wai-Yip; Côté, Nathalie; Falk, Tiago H.; Raake, Alexander; Wältermann, Marcel

doi:10.1109/msp.2011.942469

Cited by 124 publications

(74 citation statements)

References 24 publications

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“…For example, the transmission rating scale of the E-model quantifies the quality of speech transmission on a rating scale from 0, … , 100 . This scale is extended to 0, … , 129 to consider wideband transmission [30]. Consequently, we argue that a QoE fairness index needs to quantify fairness independent of the underlying scale.…”

Section: Desirable Properties Of a Qoe Fairness Indexmentioning

confidence: 99%

A new QoE fairness index for QoE management

et al. 2018

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The user-centric management of networks and services focuses on the Quality of Experience (QoE) as perceived by the end user. In general, the goal is to maximize (or at least ensure an acceptable) QoE, while ensuring fairness among users, e.g., in terms of resource allocation and scheduling in shared systems. A problem arising in this context is that the notions of fairness commonly applied in the QoS domain do not translate well to the QoE domain. We have recently proposed a QoE fairness index F, which solves these issues. In this paper, we provide a detailed rationale for it, along with a thorough comparison of the proposed index and its properties against the most widely used QoS fairness indices, showing its advantages. We furthermore explore the potential uses of the index, in the context of QoE management and describe future research lines on this topic.

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Section: Desirable Properties Of a Qoe Fairness Indexmentioning

confidence: 99%

A new QoE fairness index for QoE management

et al. 2018

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“…A complete description of the benchmark algorithms is beyond the scope of this paper and the interested readers are referred to [22] and to the references given hereafter for more details. The Perceptual Evaluation of Speech Quality (PESQ) [23] and Perceptual Objective Listening Quality Assessment (POLQA) [24] are both ITU-T standards for intrusive speech quality measurement.…”

Section: Benchmark Metricsmentioning

confidence: 99%

An improved non-intrusive intelligibility metric for noisy and reverberant speech

Santos

Senoussaoui

Falk

2014

2014 14th International Workshop on Acoustic Signal Enhancement (IWAENC)

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Non-intrusive speech intelligibility metrics are based solely on the corrupted speech information and a prior model of the speech signal in a given representation. As such, any sources of variability not taken into account by the model will affect the metric's performance. In this paper, we investigate two sources of variability in the auditory-inspired model used by the speech-to-reverberation modulation energy ratio (SRMR) metric, namely speech content and pitch, and propose two updates that aim to reduce the variability caused by these sources. First, we limited the dynamic range of the energies in the modulation spectrum bands in order to reduce the effect of speech content and speaker variability. Second, the range of the modulation filter bank was modified to reduce the variability due to pitch. Experimental results show that the updated metric presents higher performance and lower variability relative to the original SRMR when assessing speech intelligibility in noisy and reverberant environments, as well as outperforms several standard intrusive and non-intrusive benchmark metrics.

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“…features [3]. The quality of a speech signal can be obtained in a listening test with a number of human subjects (subjectbased methods) or algorithmically (instrumental methods).…”

Section: Introductionmentioning

confidence: 99%

“…A number of nonintrusive techniques have been proposed over the past decade, see [13], [3] for a review. The current ITU-T industry standard algorithm for non-intrusive speech quality assessment is P.563 [15], which uses a number of features from the audio stream to estimate the quality score directly on the MOS scale.…”

Section: Introductionmentioning

confidence: 99%