Unsupervised classification of speaker roles in multi-participant conversational speech. (March 2017)

• Record Type: Journal Article
• Authors: Li, Yanxiong; Wang, Qin; Zhang, Xue; Li, Wei; Li, Xinchao; Yang, Jichen; Feng, Xiaohui; Huang, Qian; He, Qianhua
• Is Part Of: Computer speech & language. Volume 42(2017)
• Page Start: 81
• Page End: 99
• Publication Date: 2017-03
• Subjects: Speaker role -- Speaker diarization -- Role clustering -- Multi-participant conversational speech; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2016.09.002
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: We propose an unsupervised and universal method for speaker roles classification. We extract features from turns, speech segments and both of them for speaker role classification. We propose a role clustering algorithm based on maximizing inter-cluster distances. Abstract: This paper proposes an unsupervised method for analyzing speaker roles in multi-participant conversational speech. First, features for characterizing the differences of various roles are extracted from the outputs of speaker diarization. Then, an algorithm of role clustering based on the criterion of maximizing the inter-cluster distance without using any convergence threshold is proposed to obtain the number of roles and to merge the utterances belonging to the same role into one cluster. The contributions of different combinations of individual feature subsets are compared for the proposed method on the outputs from speaker diarization, and the combined feature subsets obtain higher F scores than the individual ones for clustering speaker roles. The impacts of both speaker diarization errors and feature dimensions on the performance of the proposed method are also discussed. Experiments are done on the outputs of both manual annotations and automatic speaker diarization to compare the proposed method with both the state-of-the-art clustering method and the supervised method. Evaluations show that the proposed method is superior to the previous clustering method and close to the conventional supervised method in terms of F scores under two different experimental conditions.