Review of recent standardization activities in speech quality of experience

Möller, Sebastian; Köster, Friedemann

doi:10.1007/s41233-017-0012-7

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…In addition, the interested reader is referred to the overview pages of the corresponding ITU-T [48], [49], ITU-R [333], [334] and ISO MPEG [50] standards to get up-todate information about standardized methods. Scientific texts such as [4], [20], [335] as well as text books and PhD theses on quality assessment of interactive telecommunication services (e.g., [7], [28], [51], [61], [82], [95], [246], [336] give further pointers to standardized and non-standardized methods that are relevant for telemeeting assessment. For surveys of QoE assessment methods for other services such as HTTPbased adaptive streaming, see, for example, [337]- [339] or generally for audiovisual multimedia, see [20].…”

Section: Test Factormentioning

confidence: 99%

Quality of Experience in Telemeetings and Videoconferencing: A Comprehensive Survey

et al. 2022

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Section: Test Factormentioning

confidence: 99%

Quality of Experience in Telemeetings and Videoconferencing: A Comprehensive Survey

et al. 2022

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“…This makes the design of NI-SQA techniques more challenging. The standard method for NI-SQA is described in ITU-T Recommendation P.563 [ 6 ], but it only shows low correlations with subjective quality ratings when speech quality other than narrow-band transmission is considered [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

NISQE: Non-Intrusive Speech Quality Evaluator Based on Natural Statistics of Mean Subtracted Contrast Normalized Coefficients of Spectrogram

Zafar

Nizami

Rehman

et al. 2023

Sensors

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With the evolution in technology, communication based on the voice has gained importance in applications such as online conferencing, online meetings, voice-over internet protocol (VoIP), etc. Limiting factors such as environmental noise, encoding and decoding of the speech signal, and limitations of technology may degrade the quality of the speech signal. Therefore, there is a requirement for continuous quality assessment of the speech signal. Speech quality assessment (SQA) enables the system to automatically tune network parameters to improve speech quality. Furthermore, there are many speech transmitters and receivers that are used for voice processing including mobile devices and high-performance computers that can benefit from SQA. SQA plays a significant role in the evaluation of speech-processing systems. Non-intrusive speech quality assessment (NI-SQA) is a challenging task due to the unavailability of pristine speech signals in real-world scenarios. The success of NI-SQA techniques highly relies on the features used to assess speech quality. Various NI-SQA methods are available that extract features from speech signals in different domains, but they do not take into account the natural structure of the speech signals for assessment of speech quality. This work proposes a method for NI-SQA based on the natural structure of the speech signals that are approximated using the natural spectrogram statistical (NSS) properties derived from the speech signal spectrogram. The pristine version of the speech signal follows a structured natural pattern that is disrupted when distortion is introduced in the speech signal. The deviation of NSS properties between the pristine and distorted speech signals is utilized to predict speech quality. The proposed methodology shows better performance in comparison to state-of-the-art NI-SQA methods on the Centre for Speech Technology Voice Cloning Toolkit corpus (VCTK-Corpus) with a Spearman’s rank-ordered correlation constant (SRC) of 0.902, Pearson correlation constant (PCC) of 0.960, and root mean squared error (RMSE) of 0.206. Conversely, on the NOIZEUS-960 database, the proposed methodology shows an SRC of 0.958, PCC of 0.960, and RMSE of 0.114.

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A feature selection for video quality of experience modeling: A systematic literature review

Skaka-Čekić

Husić

2023

WIREs Data Min & Knowl

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Quality of Experience (QoE) multidimensional concept is the key for successful delivery of multimedia services. Higher user requirements for new experiences such as augmented reality, virtual reality, and future 6G services set higher requirements for QoE. A more complex QoE space requires the use of data mining methods in order to process the data for better QoE prediction. The increased dimensionality of the QoE space becomes a limiting factor for achieving the desired QoE prediction accuracy. Existing studies considering the QoE multidimensional concept with approaches that overcome the challenge of increased QoE space dimensionality are of great importance for future research. Accordingly, this article aims to review the applications of Feature Selection (FS) methods in video QoE modeling. It provides a comprehensive overview of the existing studies with the categorization and review of applied FS methods with reference to the data collection and data modeling steps. The analysis included 71 studies which provides overview of the FS methods applications in video QoE modeling depending on the input Influence Factor (IF) dimension sizes, type of IFs, QoE prediction methods used and QoE evaluation type. Our review revealed the advantages of using FS methods in video QoE modeling, frequency of application of FS methods with potential of applying more FS methods in a series or a parallel, gives an overview of the achieved dimensionality reduction degree for different methods, and provides insights in opportunities for researchers for applying FS methods on complex multidimensional QoE space. This article is categorized under: Technologies > Data Preprocessing Algorithmic Development > Multimedia

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Review of recent standardization activities in speech quality of experience

Cited by 3 publications

References 17 publications

Quality of Experience in Telemeetings and Videoconferencing: A Comprehensive Survey

Quality of Experience in Telemeetings and Videoconferencing: A Comprehensive Survey

NISQE: Non-Intrusive Speech Quality Evaluator Based on Natural Statistics of Mean Subtracted Contrast Normalized Coefficients of Spectrogram

A feature selection for video quality of experience modeling: A systematic literature review

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