Double Joint Bayesian Modeling of DNN Local I-Vector for Text Dependent Speaker Verification with Random Digit Strings

Abstract

Double joint Bayesian is a recently introduced analysis method that models and explores multiple information explicitly from the samples to improve the verification performance. It was recently applied to voice pass phrase verification, result in better results on text dependent speaker verification task. However little is known about its effectiveness in other challenging situations such as speaker verification for short, text-constrained test utterances, e.g. random digit strings. Contrary to conventional joint Bayesian method that cannot make full use of multi-view information, double joint Bayesian can incorporate both intra-speaker/digit and inter-speaker/digit variation, and calculated the likelihood to describe whether the features having all labels consistent or not. We show that double joint Bayesian outperforms conventional method on modeling DNN local (digit-dependent) i-vectors for speaker verification with random prompted digit strings. Since the strength of both double joint Bayesian and conventional DNN local i-vector appear complementary, the combination significantly outperforms either of its components.

Event-driven Pipeline for Low-latency Low-compute Keyword Spotting and Speaker Verification System

“…Here, we address the problem of keyword spotting in a more challenging setting with competing talkers. Recent studies tackle KWS for both efficient computation [4] and small footprint [6,7], while not many studies address these problems in the context of SV [3,2]. Efforts exist for either jointly solving both tasks [5] or solving a single task in the presence of background noise [10].…”

“…The demand for personalized voice-activated devices has rapidly grown in recent years. Along with this, we see increasing research in algorithms useful for these devices such as speaker verification (SV), and keyword spotting (KWS) [1,2,3].…”