Advances in unsupervised audio segmentation for the broadcast news and NGSW corpora

The problem of unsupervised audio segmentation continues to be a challenging research problem which significantly impacts automatic speech recognition (ASR) and spoken document retrieval (SDR) performance. This paper addresses novel advances in audio segmentation for unsupervised multi-speaker change detection. First, we investigate new features which are intended to be more appropriate for segmentation that include: PMVDR (perceptual minimum variance distortionless response), SZCR ( smoothed zero crossing rate), and FBLC (filterbank log coefficients); next we consider a new distance metric, T²-mean which is intended to improve segmentation for short segments (<5s). A novel false alarm compensation procedure is also developed and used after the segmentation phase. We establish a more effective evaluation procedure for segmentation versus the more traditional EER and frame accuracy approaches. Employing these advances within our new scheme, results in more than a 30% improvement in segmentation performance using the 3-hour Hub4 broadcast news 1997 evaluation data. Evaluations are also presented for audio from the NGSW corpus.