Who Spoke What? A Latent Variable Framework for the Joint Decoding of Multiple Speakers and their Keywords

Sundar, Harshavardhan and Sreenivas, Thippur V (2016) Who Spoke What? A Latent Variable Framework for the Joint Decoding of Multiple Speakers and their Keywords. In: 11th International Conference on Signal Processing and Communications (SPCOM), JUN 12-15, 2016, Indian Inst Sci, Banglore, INDIA.

PDF 2016_11th_Int_Con_SPCOM_2016.pdf - Published Version Restricted to Registered users only Download (233kB) | Request a copy

Abstract

In this paper, we present a latent variable (LV) framework to identify all the speakers and their keywords given a single channel microphone recording containing a multispeaker mixture signal. We introduce two separate LVs to denote active speakers and the keywords uttered. The dependency of a spoken keyword on the speaker is modeled through a conditional probability mass function. The distribution of the mixture signal is expressed in terms of the LV mass functions and speaker-specific-keyword models. The proposed framework admits stochastic models, representing the probability density function of the observation vectors given that a particular speaker uttered a specific keyword, as speaker-specific-keyword models. The LV mass functions are estimated in a Maximum Likelihood framework using the Expectation Maximization (EM) algorithm. The active speakers and their keywords are detected as modes of the joint distribution of the two LVs. With Student's-t Mixture Models (tMMs) as speaker specific keyword models, the proposed approach is able to detect at least one speaker-keyword pair, in mixture signal with two speakers, with an accuracy of 99% and both speaker-keyword pairs, with an accuracy of 82%.

Item Type:	Conference Proceedings
Additional Information:	Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering Division of Electrical Sciences > Electrical Engineering
Date Deposited:	31 Jan 2017 05:33
Last Modified:	31 Jan 2017 05:33
URI:	http://eprints.iisc.ac.in/id/eprint/56157

Actions (login required)

View Item