We report a new fusion based segmentation approach using multiple filter bank coefficients. This approach takes advantage of current feature extraction procedure, with little additional computation cost. Another level of fusion was performed by combining several segmentation systems. Evaluation was conducted on the second Speech In Noisy Environments (SPINE2) task. Experiments show our fusion based approaches significantly reduced the WER compared to two classifier-based approaches. Compared to the manual segmentation, our approach only has 0.3% WER increase.
['Chengyi Zheng', 'Yonghong Yan']
Fusion based speech segmentation in DARPA SPINE2 task