Non-intrusive Speech Quality Assessment

Algorithm Based on Spectro-temporal Analysis

School of Computer Engineering

LI Qiaohong

(Supervisor: Prof Weisi Lin)

(Co-Supervisor: Prof Daniel THALMANN)

July. 22, 2014

Outline

• Motivation

• Review

• Method

• Experimental results

• Conclusion and future work

Motivation

Speech

Signals

6. Transmission(eg. VoIP, IPTV)

1. Acquisition

(Noise)

3. Reproduction(eg. imperfect

reconstruction)

5. Postprocessing(eg. enhancement)

2. Synthesis(eg. Text-to-speech)

4. Security(eg. watermarking)

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• Applications of speech quality assessment methods:

• speech acquisition, enhancement, watermarking, compression,

transmission, reconstruction, authentication, speech synthesis …

• Two broad approaches:

• subjective vs. objective methods

• Subjective assessment suffers from drawbacks

• time-consuming, laborious and expensive; requires many human subjects

and repeated viewing/listening sessions

• Not feasible for on-line signal manipulations (such as encoding,

transmission, relaying, etc.)

• depends upon viewers’ physical conditions, emotional states, personal

experience etc

Motivation

Objective Speech Quality Assessment

• Intrusive SQA methods(a.k.a double-ended, full-reference methods)

Parametric based methods

Using the parameters of the compression and transmission protocols to estimate the final quality score.

Signal based methods

Calculating the perceptually weighted distance between the reference and

degraded speech signals.

Eg. SNR, LLR, BSD, PSQM, PESQ, POLQA.

• Non-intrusive SQA methods• (a.k.a single-ended, no-reference)

Engineering Approach Framework

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Feature extraction

Reference signal

Distorted signal

Feature pooling

(cognitive mapping)Quality score

Stage I Stage II

For intrusive methods

Exploits signal processing

techniques

Based on machine learning

Gabor Feature Extraction for Speech

Quality Assessment

Gabor feature extraction pipeline

Gabor Feature Extraction for Speech

Quality Assessment

The effectiveness of extracted Gabor features

SVR for feature mapping

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We adopt the SVR to learn the mapping from extracted Gabor features to

objective speech quality

We use the Radial Basis Function (RBF) kernel with the kernel

function of 𝐾(𝒙𝑖, 𝒙𝑗) = exp(−𝜌 ∥𝒙𝑖 − 𝒙𝑗∥2) in this work. The

parameters {𝐶, 𝜌, 𝜖} are selected through cross validation

80% Training data vs. 20% Test data

Overall Test

Split dataset according to different contents

Test 1

Split dataset according to different noise levels

Test 2

Split dataset according to different noise types

Test 3

Split dataset according to different noise enhanced algorithms

The scatter plot of the perdition results of proposed metric

versus the subjective scores in NOIZEUS database

Experimental results

Comparison with state-of-the-arts

[7] T. H. Falk and Chan Wai-Yip, “Single-ended speech quality measurement using machine learning methods,” Audio, Speech, and Language Processing, IEEE Transactions on, vol. 14, no. 6, pp. 1935–1947, 2006

[10] M. Narwaria, Lin Weisi, I. V. McLoughlin, S. Emmanuel, and Chia Liang-Tien, “Nonintrusive quality assessment of noise suppressed speech with mel-filtered energies and support vector regression,” Audio, Speech, and Language Processing, IEEE Transactions on, vol. 20, no. 4, pp. 1217–1232, 2012

Experimental results

Future Work

No-reference visual quality assessment

Joint audiovisual quality assessment:

humans perceive ‘overall’ multimedia quality and not separate assessment

Possible approaches include one-stage and two-stage fusion (OSF/TSF)

OSF: both audio and speech features pooled in one stage

TSF: first pool audio, then video features and the two scores into an overall score

Thank you!

Questions?

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