Artificial Signals and Identification Methods to Evaluate the Quality of Speech Coders

Objective quality measures provide an economic and practical alternative to tedious and expensive subjective tests. Furthermore, some recently proposed measures predict quality values which correlate quite well with the subjective ones. The objective measures are particularly useful to test the influence of design parameters during the laboratory development of codec algorithms. In this paper the architecture of a measuring equipment to conduct objective quality measures on speech waveform coders is presented and discussed. Only the input and output signals from the coder are needed and an identification algorithm is used to separate the distortion effects introduced by the coder from other effects such as gains, time shifts, low-pass filtering, and so on. The choice of the most appropriate kind of signal for the identification and measurement steps is a problem also investigated. Particularly, results concerning mathematically defined speech-like signals are reported. The performance of the method is investigated under different conditions and the correlations with subjectively determined results are also reported for a number of ADPCM coders with widely varying parameters.