Latent class model with application to speaker diarization

Liang He,Can Xu,Jia Liu,Xianhong Chen,Yi Liu,Michael T Johnson

doi:10.1186/s13636-019-0154-z

Liang He, Can Xu + Show 4 more

Open Access

https://doi.org/10.1186/s13636-019-0154-z

Copy DOI

Abstract

In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny’s variational Bayes (VB) method in that it uses soft information and avoids premature hard decisions in its iterations. In contrast to the VB method, which is based on a generative model, LCM provides a framework allowing both generative and discriminative models. The discriminative property is realized through the use of i-vector (Ivec), probabilistic linear discriminative analysis (PLDA), and a support vector machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid are introduced. In addition, three further improvements are applied to enhance its performance. (1) Adding neighbor windows to extract more speaker information for each short segment. (2) Using a hidden Markov model to avoid frequent speaker change points. (3) Using an agglomerative hierarchical cluster to do initialization and present hard and soft priors, in order to overcome the problem of initial sensitivity. Experiments on the National Institute of Standards and Technology Rich Transcription 2009 speaker diarization database, under the condition of a single distant microphone, show that the diarization error rate (DER) of the proposed methods has substantial relative improvements compared with mainstream systems. Compared to the VB method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments on our collected database, CALLHOME97, CALLHOME00, and SRE08 short2-summed trial conditions also show that the proposed LCM-Ivec-Hybrid system has the best overall performance.

Highlights

Speaker diarization task aims to address the problem of “who spoke when” in an audio stream by splitting the audio into homogeneous regions labeled with speaker identities [1]
We introduce the probabilistic linear discriminative analysis (PLDA) and support vector machine (SVM) into the computation, and propose latent class model (LCM)-IvecPLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid systems
It can be seen that the performance of LCM systems is better than that of Bayesian information criterion (BIC) system

Summary

Introduction

In the field of speaker diarization, variational Bayes (VB) proposed by Patrick Kenny [2,3,4,5] and VB-hidden Markov model (HMM) introduced by Mireia Diez [6] have become the state-of-the-art approaches. The mainstream approach to speaker segmentation is finding speaker change points based on a similarity metric This includes Bayesian information criterion (BIC) [15], Kullback-Leibler [16], generalized likelihood ratio (GLR) [17], and i-vector/PLDA [18]. 2.1 Bottom-up approach The bottom-up approach is the most popular one in speaker diarization [11], which is often referred to as an agglomerative hierarchical clustering (AHC) This approach treats each segment, divided by speaker change points, as an individual cluster, and merges a pair of clusters into a new one based on the nearest neighbor criteria. Compared with the bottom-up or top-down approach, the VB approach uses a soft decision strategy and avoids a premature hard decision

1: Voice activity detection and feature extraction 2

Further improvements

Related work and discussion

Experiments Experiments have been implemented on five databases

Method