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Feature set augmentation for enhancing the performance of a non-intrusive quality predictor
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
2012 (English)In: 2012 4th International Workshop on Quality of Multimedia Experience, QoMEX 2012, IEEE , 2012, 121-126 p.Conference paper, Published paper (Refereed)
Abstract [en]

A non-intrusive quality predictor constitutes a mapping from signal features to a (typically one dimensional) representation of the perceived quality. Assuming that the regression model performing the mapping is suited to the data, the performance of the predictor largely depends on how well the parameters of this regression model can be inferred from the training data. In situations where the training data is scarce, model performance is degraded due to over-fitting. The effects of over-fitting can be mitigated by feature selection but the model performance remains low due to the insufficiently representative training data. The objective we pursue is to enhance the performance of a quality predictor by augmenting the feature set with the output of a pre-trained quality predictor. This approach introduces an implicit dependence of the regression model parameters on a larger amount of training data. In view of the increasing usage of speech signals with higher bandwidth, and the dearth of training data for such signals, an augmentation of particular interest is that of a wide-band feature set with a narrow-band quality prediction. Experimental results for additive noise and non-linear distortions encountered in hearing aids, using quality labels from an intrusive quality predictor, illustrate the performance enhancement capabilities of the proposed approach.

Place, publisher, year, edition, pages
IEEE , 2012. 121-126 p.
Keyword [en]
input uncertainty, machine learning with Gaussian processes, Non-intrusive quality assessment
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-105308DOI: 10.1109/QoMEX.2012.6263856Scopus ID: 2-s2.0-84866646261ISBN: 978-146730725-3 (print)OAI: oai:DiVA.org:kth-105308DiVA: diva2:570637
Conference
2012 4th International Workshop on Quality of Multimedia Experience, QoMEX 2012, 5 July 2012 through 7 July 2012, Melbourne, VIC
Funder
ICT - The Next Generation
Note

QC 20121120

Available from: 2012-11-20 Created: 2012-11-20 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
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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
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