A Bayesian-Maximum Entropy Approach to Subjective Voice Quality Testing

In order to assess the performance of Internet telephony, it is often necessary to translate network impairments (such as packet loss, delay and jitter) into human perceived quality (which is quantified in terms of subjective voice quality ratings). Subjective quality testing is expensive and typically involves a large number of questions and humans. It is therefore important to design simple and reliable subjective testing experiments. This paper presents a method to assess the subjective quality of a number of speech samples that have incurred various degrees of the same network impairment. Questions are asked according to an adaptive algorithm until all voice ratings are elicited within a desired accuracy. Our algorithm (i) uses information theory to minimize the expected number of questions needed and (ii) uses binary questions, which are simpler than the types of questions used by standard subjective testing procedures.