Abstract—Audio quality in the Internet can be strongly
affected by network conditions. As a consequence, many
techniques to evaluate it have been developed. In particular, the
ITU-T adopted in 2001 a technique called Perceptual Evaluation
of Speech Quality (PESQ) to automatically measuring speech
quality. PESQ is a well-known and widely used procedure,
providing in general an accurate evaluation of perceptual
quality by comparing the original and received voice sequences.
One obvious inherent limitation of PESQ is, thus, that it
requires the original signal (we say the reference), to make its
evaluation. This precludes the use of PESQ for assessing the
perceived quality in real-time, as the reference is in general not
available.
In this paper, we describe a procedure for estimating PESQ
output working only with measures taken on the network state
and properties of the communication system, without any use of
the reference. It is based on the use of statistical learning
techniques. Specifically, we rely on recent ideas for learning
with specific types of neural networks, known under the name of
Echo State Networks (ESNs), a member of the class of Reservoir
Computing systems. These tools have been proven to be very
efficient and robust in many learning tasks. The experimental
results obtained show the good accuracy of the resulting
procedure, and its capability to give its estimations of speech
quality in a real-time context. This allows putting our measuring
modules in future Internet applications or services based on
voice transmission, for instance for control purposes
Index Terms—Quality assessment, speech quality, echo state
networks, reservoir computing.
S. Basterrech is with the University of Rennes 2, Rennes, France (e-mail:
Sebastian.Basterrechtiscordio@etudiant.uhb.fr).
G. Rubino is with the National Institute for Research in Computer
Science and Control (INRIA Rennes – Bretagne Atlantique), Rennes, France
(e-mail: Gerardo.Rubino@inria.fr).
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Cite:Sebastián Basterrech and Gerardo Rubino, "Real-Time Estimation of Speech Quality through the Internet Using Echo State Networks," Journal of Advances in Computer Networks vol. 1, no. 3, pp. 183-188, 2013.
