Proc. International Technical Conference on Circuits/Systems Computers and Communications (ITC-CSCC)
Volume, Number, Page
pp. 27-30
Published date
June 29, 2015
Publisher
Japanese:
English:
Conference name
Japanese:
English:
The 30th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) 2015
Conference site
Japanese:
ソウル
English:
Seoul
Abstract
We propose multi-modal i-vectors, which extend the audio i-vector framework for speaker verification to a multi-modal speaker diarization in movies. In addition to the audio i-vector, which represents a speech utterance in an audio stream by a low-dimensional vector, we extract a visual i-vector from faces in a video segment. The audio and visual i-vectors are concatenated as a multi-modal i-vector clustered in an unsupervised way. We evaluate our method on the Hannah movie dataset. Our experiments show that diarization error rate is improved from 68.3% to 65.5% compared with audio stream only.