Audio segmentation has applications in a variety of contexts, such as audio information retrieval, automatic sound analysis, and as a pre-processing step in speech recognition. Extended Baum-Welch (EBW) transformations are most commonly used as a discriminative technique for estimating parameters of Gaussian mixtures. In this paper, we derive an unsupervised audio segmentation approach using these transformations. We find that our algorithm outperforms both the Bayesian information criterion (BIC) and cumulative sum (CUSUM) segmentation methods. In particular, our EBW segmentation algorithm provides improvements over the baseline approaches in detecting landmarks of short duration and minimizing landmark oversegmentation. In addition, we show that the EBW approach provides faster computation compared to the baseline methods.
['Tara N. Sainath', 'Dimitri Kanevsky', 'Giridharan Iyengar']
Unsupervised Audio Segmentation using Extended Baum-Welch Transformations