KNNDIST: A Non-Parametric Distance Measure for Speaker Segmentation

A novel distance measure for distance-based speaker segmentation is proposed. This distance measure is nonparametric, in contrast to common distance measures used in speaker segmentation systems, which often assume a Gaussian distribution when measuring the distance betweentwo audio segments. This distance measure is essentially a k-nearestneighbor distance measure. Non-vowel segment removal in preprocessing stage is also proposed. Speaker segmentation performance is tested on artificially created conversations from the TIMIT database and two AMI conversations. For short window lengths, Missed Detection Rated is decreased significantly. For moderate window lengths, a decrease in both Missed Detection and False Alarm Rates occur. The computational cost of the distance measure is high for long window lengths.