Google Research

Speech/Nonspeech Segmentation in Web Videos

Proceedings of InterSpeech 2012

Abstract

Speech transcription of web videos requires first detecting segments with transcribable speech. We refer to this as segmentation. Commonly used segmentation techniques are inadequate for domains such as YouTube, where videos may have a large variety of background and recording conditions. In this work, we investigate alternative audio features and a discriminative classifier, which together yield a lower frame error rate (25.3%) on YouTube videos compared to the commonly used Gaussian mixture models trained on cepstral features (30.6%). The alternative audio features perform particularly well in noisy conditions.

Research Areas

Learn more about how we do research

We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work