Speaker independent discriminant feature extraction for acoustic pattern-matching

Acoustic pattern-matching algorithms have recently become prominent again for automatically processing speech utterances where no prior knowledge of the spoken language is required. Applications of such technology include, but are not limited to, query-by-example search, spoken term detection and automatic word discovery. Obtaining content-aware acoustic features as independent as possible from speaker and acoustic environment variations is a key step in these algorithms. Currently, GMM posteriorgrams are found to outperform the standard MFCC features even though they were not designed to optimize the discrimination between acoustic classes. In this paper we combine the K-means clustering algorithm with the GMM posteriorgrams front-end to obtain more discriminant features. Results on a query-by-example task show that the proposed approaches outperform standard MFCC features by 7.8% absolute P@N and GMM-based posteriorgram features by 3.7% absolute P@N when using a 64-dimensional feature vector.