Real-Time Estimation of Speech Quality through the Internet Using Echo State Networks

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.