Proceedings ArticleDOI
A novel output-based objective speech quality measure for wireless communication
TLDR
Two output-based objective speech measures which are based on visual features of the spectrogram are proposed which achieve high correlation when used to predict subjective mean opinion scores (MOS) of real cellular telephone speech samples.Abstract:
In our previous papers, we studied many input-to-output objective speech quality measures, some of which achieved high correlation when used to predict subjective mean opinion scores (MOS) of real cellular telephone speech samples. Two problems of input-to-output measures are that the input must be available, which is almost never the case in the cellular telephone situation, and the input must be accurately synchronized with the output. Output-based measures which do not need the input are thus highly desirable. In this paper, we propose two output-based objective speech measures which are based on visual features of the spectrogram. In our experiment, one measure OBM achieves a correlation of 0.65 which is higher than most input-to-output measures and is close to the 0.73 achieved by the best input-to-output measure.read more
Citations
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Journal ArticleDOI
Low-Complexity, Nonintrusive Speech Quality Assessment
TL;DR: A low-complexity algorithm for monitoring the speech quality over a network that can be computed from commonly used speech-coding parameters without explicit distortion modeling is described.
Journal ArticleDOI
Audio-Visual Multimedia Quality Assessment: A Comprehensive Survey
Zahid Akhtar,Tiago H. Falk +1 more
TL;DR: A comprehensive survey of the works that have been carried out over recent decades in perceptual audio, video, and joint audio-visual quality assessments is provided, describing existing methodologies in terms of requirement of a reference signal, feature extraction, feature mapping, and classification schemes.
Book ChapterDOI
Speech Quality Assessment
TL;DR: This chapter provides an overview of methods for speech quality assessment with a focus on standardized methods, and defines the term speech quality and outlines the main causes of degradation of speech quality.
Proceedings ArticleDOI
Novel deep autoencoder features for non-intrusive speech quality assessment
Meet H. Soni,Hemant A. Patil +1 more
TL;DR: Quantification of the experimental results suggests that proposed metric gives more accurate and correlated scores than an existing benchmark for objective, non-intrusive quality assessment metric ITU-T P.563 standard.
Journal ArticleDOI
Nonintrusive Quality Assessment of Noise Suppressed Speech With Mel-Filtered Energies and Support Vector Regression
TL;DR: This paper proposes a nonintrusive metric for the quality assessment of noise-suppressed speech and utilizes the sensitivity of FBEs to noise in order to obtain an effective representation of speech towards quality assessment.
References
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Proceedings ArticleDOI
Output-based objective speech quality
Jin Liang,R. Kubichek +1 more
TL;DR: Experiments are described to develop a new technique that requires only the received speech, which uses perceptually-based speaker-independent speech parameters such as perceptual-linear prediction coefficients and the perceptually weighted Bark spectrum to estimate subjective quality.
Proceedings ArticleDOI
Objective speech quality measure for cellular phone
TL;DR: An experiment to collect real distorted data, a survey to obtain subjective quality measure of the collected speech samples and the statistical correlation of 32 objective speech quality measures with the subjective measures found four of the objective measures to be good.
Proceedings ArticleDOI
From phonology and acoustic properties to automatic recognition of Cantonese
P.C. Ching,T. Lee,E. Zee +2 more
TL;DR: The recent development in designing efficient algorithms based on artificial neural networks for tone classification and syllable recognition of Cantonese are described.
Proceedings ArticleDOI
Feature extraction from speech spectrograms using multi-layered network models
M.J. Palakal,M.J. Zoran +1 more
TL;DR: The authors propose a method for capturing speaker-invariant features from speech spectrograms using artificial neural network (ANN) models and initial test results show that the network model is capable of learning all important features that are present in the pattern studied.
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