Output-based speech quality (OBQ) refers to objective speech quality assessment using only received speech without utilizing the input speech record. This paper proposes three new OBQ measures and evaluates their performance. Parameters derived from perceptual linear prediction (PLP) coefficients are used to provide speaker independence required by the objective measures. PLP, PLP cepstrum, and PLP delta-cepstrum parameters are computed for output speech records from an undistorted source speech database and vector quantized. The resulting codebook provides a reference for computing objective distance measures for distorted speech. The proposed objective measures are the transition probability distance, the median minimum distance, and the chi-squared distance. The OBQ parameters are tested on four different speech datasets, and correlation is computed between subjective scores and objective distances under a variety of conditions. The results indicate that the proposed algorithms are robust against speaker, text, and distortion variation.

Output-based speech quality (OBQ) refers to an objective speech quality measure that uses only received speech without access to the input speech record. This paper proposes two new OBQ measures and evaluates their performance. Perceptual linear prediction (PLP) coefficients are used to provide speaker independence required by the objective measure. Two distortion measures are introduced for predicting speech quality based on vector quantization of the output speech record. These are the transition probability distance and the median minimum distance measure. The OBQ parameters are tested on four different speech datasets. The correlation is computed between subjective scores and the objective quality measures under a variety conditions, and the results indicate that the proposed algorithms are quite robust against speaker, text and distortion variation.