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Probabilistic non-intrusive quality assessment of speech for bounded-scale preference scores
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
2010 (English)In: 2010 2nd International Workshop on Quality of Multimedia Experience, 2010, Vol. QoMEX 2010 - Proceedings, 188-193 p.Conference paper, Published paper (Refereed)
Abstract [en]

We propose a probabilistic, non-intrusive method for quality assessment of speech that takes into consideration the bounded character of the preference scores. The quality ratings are modeled as iid Beta random variables, whose mean and precision are parametrized directly in terms of the signal features. Maximum likelihood estimation is used to learn the model parameters in view of a training database. Given a valuation point, the proposed model produces a distribution over the range of allowed quality ratings, which can be used to evaluate the statistics of interest. The model performance, in terms of correlation and root mean square error, compares favorably to the state-of-the-art in the field. Low computational complexity in training and prediction make the model attractive for a wide range of applications. The usage of band-based features in the feature set facilitates extension of the proposed model to input signals with larger bandwidth.

Place, publisher, year, edition, pages
2010. Vol. QoMEX 2010 - Proceedings, 188-193 p.
Keyword [en]
Beta regression, Maximum likelihood, Non-intrusive quality assessment, Feature sets, Input signal, Model parameters, Model performance, Non-intrusive, Non-intrusive method, Quality assessment, Quality ratings, Root mean square errors, Signal features, Training database, Computational complexity, Random variables, Maximum likelihood estimation
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-36316DOI: 10.1109/QOMEX.2010.5516236Scopus ID: 2-s2.0-77955761725ISBN: 9781424469604 (print)OAI: oai:DiVA.org:kth-36316DiVA: diva2:430671
Conference
2010 2nd International Workshop on Quality of Multimedia Experience, QoMEX 2010; Trondheim
Note
QC 20110712Available from: 2011-07-12 Created: 2011-07-11 Last updated: 2014-05-23Bibliographically approved
In thesis
1. Improving Quality of Service in Baseband Speech Communication
Open this publication in new window or tab >>Improving Quality of Service in Baseband Speech Communication
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Speech is the most important communication modality for human interaction. Automatic speech recognition and speech synthesis have extended further the relevance of speech to man-machine interaction. Environment noise and various distortions, such as reverberation and speech processing artifacts, reduce the mutual information between the message modulated inthe clean speech and the message decoded from the observed signal. This degrades intelligibility and perceived quality, which are the two attributes associated with quality of service. An estimate of the state of these attributes provides important diagnostic information about the communication equipment and the environment. When the adverse effects occur at the presentation side, an objective measure of intelligibility facilitates speech signal modification for improved communication.

The contributions of this thesis come from non-intrusive quality assessment and intelligibility-enhancing modification of speech. On the part of quality, the focus is on predictor design for limited training data. Paper A proposes a quality assessment model for bounded-support ratings that learns efficiently from a limited amount of training data, scales easily with the sampling frequency, and provides a platform for modeling variations in the individual subjective ratings. The predictive performance of the model for the mean of the subjective quality ratings compares favorably to the state-of-art in the field. Patterns in the spread of the individual ratings are captured in the feature space of the training data.

Paper B focuses on enhancing predictive performance for the mean of the quality variable when the signal feature space is sparsely sampled by the training data. Using a Gaussian Processes framework, the deterministic signal-based feature set is augmented with a stochastic feature that is hypothesized to be jointly distributed with the target quality rating. An uncertainty propagation mechanism ensures that the variance of this feature is reflected in the prediction. The proposed architecture can take advantage of i) data that cannot be pooled due to subjective test protocol incompatibility and ii) models trained on data that are no longer available.

With respect to intelligibility enhancement, a hierarchical perspective of the speech communication process, extended from foundational work in the field, is used in paper C to create a unified framework for method analysis and comparison. A high-level intelligibility measure related to the probability for correct recognition is derived using a hit-or-miss distortion criterion in the transcription domain. The measure is used to optimize two speech modifications at different levels of the message encoding hierarchy leading to significantly enhanced intelligibility in noise. The conceptual novelty of the method comes at the cost of higher complexity and the requirement for additional information including message transcription, sound segmentation, and a model of speech.

Mapping the high-level measure to a lower level takes away the need for additional information and preserves asymptotically high-level optimality. Two methods are proposed to reduce degradation in the accuracy of the spectral dynamics due to additive noise. The focus of paper D is dynamics preservation in a range that is lower-bounded by an optimal band-power threshold. The performance of the method is competitive but allows for improvement in power efficiency. This issue is addressed in paper E which proposes and optimizes a distortion measure for spectral dynamics leading to a significant increase in intelligibility. Use of functional optimization techniques allows for families of solutions, among which are dynamic range compressors adaptive to the statistics of the speech and the noise.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2014. xii, 38 p.
National Category
Telecommunications
Research subject
Speech and Music Communication; SRA - ICT
Identifiers
urn:nbn:se:kth:diva-145547 (URN)978-91-7595-181-2 (ISBN)
Public defence
2014-06-12, L1, Drottning Kristinas väg 30, KTH, Stockholm, 09:00 (English)
Opponent
Supervisors
Note

QC 20140523

Available from: 2014-05-23 Created: 2014-05-21 Last updated: 2014-05-23Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf