Abstract

Speech activity detection in highly variable acoustic conditions is a challenging task. Many approaches to detect speech activity in such conditions involve an inherent knowledge of the noise types involved. Movie audio can offer an excellent research test-bed for developing speech activity models. A robust speech detection in movie audio is also a crucial step for subsequent content analyses such as audio diarization. Obtaining labels for supervision of such data can be very expensive, and may not be scalable. In this paper, we employ a simple, yet effective approach to obtain speech labels for movie data by coarse aligning the subtitles with movie audio. We compiled a dataset, called Subtitle-aligned Movie Corpus (SAM) of nearly 23 hours of data labelled as speech from ninety-five Hollywood movies. We propose convolutional neural network architectures that use log-mel spectrograms as input features to …

Date

2019

Authors

Rajat Hebbar, Krishna Somandepalli, Shrikanth Narayanan

Conference

ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

4105-4109

Publisher

IEEE