Convolutional Neural Network for speaker change detection in telephone speaker diarization system

The aim of this paper is to propose a speaker change detection technique based on Convolutional Neural Network (CNN) and evaluate its contribution to the performance of a speaker diarization system for telephone conversations. For the comparison we used an i-vector based speaker diarization system....

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Bibliographic Details
Main Authors: Hruz, Marek, Zajic, Zbynek
Format: Conference Proceeding
Language:English
Subjects:
Convolution
Convolutional Neural Network
Generalized Likelihood Ratio
Labeling
Measurement
Speaker Change Detection
Speaker Diarization
Spectrogram
Speech
Telephone sets
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Summary:The aim of this paper is to propose a speaker change detection technique based on Convolutional Neural Network (CNN) and evaluate its contribution to the performance of a speaker diarization system for telephone conversations. For the comparison we used an i-vector based speaker diarization system. The baseline speaker change detection uses Generalized Likelihood Ratio (GLR) metric. Experiments were conducted on the English part of the CallHome corpus. Our proposed CNN speaker change detection outperformed the GLR approach, reducing the Equal Error Rate relatively by 46 %. The final results on speaker diarization system indicate that the use of speaker change detection based on CNN is beneficial with relative improvement of diarization error rate by 28 %.
ISSN:2379-190X
DOI:10.1109/ICASSP.2017.7953097