IP networks are on a steep slope of innovation that will make them the long-term carrier
of all types of traffic, including voice. However, such networks are not designed to support
real-time voice communication because their variable characteristics (e.g. due to delay, delay
variation and packet loss) lead to a deterioration in voice quality. A major challenge in such networks
is how to measure or predict voice quality accurately and efficiently for QoS monitoring
and/or control purposes to ensure that technical and commercial requirements are met.
Voice quality can be measured using either subjective or objective methods. Subjective
measurement (e.g. MOS) is the benchmark for objective methods, but it is slow, time consuming
and expensive. Objective measurement can be intrusive or non-intrusive. Intrusive methods
(e.g. ITU PESQ) are more accurate, but normally are unsuitable for monitoring live traffic
because of the need for a reference data and to utilise the network. This makes non-intrusive
methods(e.g. ITU E-model) more attractive for monitoring voice quality from IP network impairments.
However, current non-intrusive methods rely on subjective tests to derive model
parameters and as a result are limited and do not meet new and emerging applications.
The main goal of the project is to develop novel and efficient models for non-intrusive
speech quality prediction to overcome the disadvantages of current subjective-based methods
and to demonstrate their usefulness in new and emerging VoIP applications. The main contributions
of the thesis are fourfold:
(1) a detailed understanding of the relationships between voice quality, IP network impairments
(e.g. packet loss, jitter and delay) and relevant parameters associated with speech (e.g.
codec type, gender and language) is provided. An understanding of the perceptual effects of
these key parameters on voice quality is important as it provides a basis for the development
of non-intrusive voice quality prediction models. A fundamental investigation of the impact of
the parameters on perceived voice quality was carried out using the latest ITU algorithm for
perceptual evaluation of speech quality, PESQ, and by exploiting the ITU E-model to obtain an
objective measure of voice quality.
(2) a new methodology to predict voice quality non-intrusively was developed. The method
exploits the intrusive algorithm, PESQ, and a combined PESQ/E-model structure to provide a
perceptually accurate prediction of both listening and conversational voice quality non-intrusively.
This avoids time-consuming subjective tests and so removes one of the major obstacles in the
development of models for voice quality prediction. The method is generic and as such has
wide applicability in multimedia applications. Efficient regression-based models and robust
artificial neural network-based learning models were developed for predicting voice quality
non-intrusively for VoIP applications.
(3) three applications of the new models were investigated: voice quality monitoring/prediction
for real Internet VoIP traces, perceived quality driven playout buffer optimization and
perceived quality driven QoS control. The neural network and regression models were both
used to predict voice quality for real Internet VoIP traces based on international links. A new
adaptive playout buffer and a perceptual optimization playout buffer algorithms are presented.
A QoS control scheme that combines the strengths of rate-adaptive and priority marking control
schemes to provide a superior QoS control in terms of measured perceived voice quality is
also provided.
(4) a new methodology for Internet-based subjective speech quality measurement which
allows rapid assessment of voice quality for VoIP applications is proposed and assessed using
both objective and traditional MOS test methods.
Date of Award | 2004 |
---|
Original language | English |
---|
Awarding Institution | |
---|
Speech quality prediction for voice over Internet protocol networks
Sun, L. (Author). 2004
Student thesis: PhD