Speaker clustering quality estimation with logistic regression. (January 2021)

• Record Type: Journal Article
• Authors: Cohen, Yishai; Lapidot, Itshak
• Is Part Of: Computer speech & language. Volume 65(2021)
• Publication Date: 2021-01
• Subjects: Clustering quality estimation -- Speaker clustering -- Mean-shift -- Stochastic vector quantization (VQ) -- Probabilistic linear discriminant analysis (PLDA) -- Cosine distance -- Logistic regression; 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.2020.101139
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: This paper focuses on estimating the quality of a clustering process. The task is to cluster short speech segments that belong to different speakers. A variety of statistical parameters are estimated from the output of the clustering process. These parameters are used to train a logistic regression to serve as a clustering quality estimation system. In this paper, mean-shift clustering with either a cosine distance or probabilistic linear discriminant analysis (PLDA) score as the similarity measure, as well as stochastic vector quantization (VQ) with cosine distance, are applied in order to cluster the short speaker segments, which are represented by i-vectors. The quality of the clustering is measured using the average cluster purity ( ACP ), average speaker purity ( ASP ) and K, which is the geometric mean of ASP and ACP . We show that these measures can be estimated fairly well by applying logistic regression. Moreover, clustering quality may be well estimated even if the logistic regression was trained using parameters derived from a different clustering algorithm. This is very important, as it allows the use of a single quality estimation system, without the need for retraining when the clustering method is changed. Additionally, we showed how the clustering quality estimator could be served as an estimator of the number of clusters. For VQ-based clustering the number of clusters has to be predefined. We perform the clustering with different number of clusters. The best number of clusters is estimated as the clustering that achieved the higher estimation of the K value. We will show that this approach estimate the best number of clusters accurately.