Title
Sequence-Level Speaker Change Detection With Difference-Based Continuous Integrate-and-Fire
Abstract
Speaker change detection is an important task in multi-party interactions such as meetings and conversations. In this paper, we address the speaker change detection task from the perspective of sequence transduction. Specifically, we propose a novel encoder-decoder framework that directly converts the input feature sequence to the speaker identity sequence. The difference-based continuous integrate-and-fire mechanism is designed to support this framework. It detects speaker changes by integrating the speaker difference between the encoder outputs frame-by-frame and transfers encoder outputs to segment-level speaker embeddings according to the detected speaker changes. The whole framework is supervised by the speaker identity sequence, a weaker label than the precise speaker change points. The experiments on the AMI and DIHARD-I corpora show that our sequence-level method consistently outperforms a strong frame-level baseline that uses the precise speaker change labels.
Year
DOI
Venue
2022
10.1109/LSP.2022.3185955
IEEE SIGNAL PROCESSING LETTERS
Keywords
DocType
Volume
Task analysis, Decoding, Training, Transforms, Recording, Predictive models, Partitioning algorithms, Difference-based continuous integrate-and-fire, sequence transduction, speaker change detection
Journal
29
ISSN
Citations 
PageRank 
1070-9908
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Zhiyun Fan100.34
linhao dong242.81
Meng Cai301.01
Zejun Ma400.68
Bo Xu511127.31