Self-segmentation of pass-phrase utterances for deep feature learning in text-dependent speaker verification. (November 2021)

• Record Type: Journal Article
• Authors: Sarkar, Achintya Kumar; Tan, Zheng-Hua
• Is Part Of: Computer speech & language. Volume 70(2021)
• Publication Date: 2021-11
• Subjects: Pass-phrases -- HMMs -- DNNs -- Bottleneck feature -- Speaker verification; 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.2021.101229
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: Propose a novel self-segmentation of pass-phrase utterances for deep feature extraction. Introduce gender-dependent, speaker-adapted phone HMMs for segmentation and label. Eliminate the need of a general-purpose, potentially-mismatched ASR for segmentation. Propose a bottleneck feature trained to discriminate gender-dependent phones. Study fusion of the proposed and existing features in score and feature domains. Abstract: In this paper, we propose a novel method to segment and label pass-phrase utterances for training deep neural network (DNN) bottleneck (BN) features for text-dependent speaker verification (TD-SV). Specifically, gender-dependent hidden Markov models (HMMs) for monophones are first trained using the pass-phrase utterances that are disjoint from evaluation. Next, the trained HMMs are speaker-adapted and then used for segmenting and labeling these training utterances at the phone level. The resulted labeled data is subsequently used for training DNN models to discriminate gender-dependent phones for the purpose of extracting phone-discriminant BN features. This is in contrast to conventional approaches that apply a general-purpose, speaker-independent automatic speech recognition (ASR) system for generating segmentation and labels. The proposed method eliminates the need for a separate ASR system, which can additionally have the disadvantage of mismatch with the pass-phrase utterances in terms languages, dialects, domains, acoustic conditions and so on. Experiments are conducted on the RedDots challenge 2016 database of TD-SV using short utterances with Gaussian mixture model-universal background model and i-vector techniques. Experimental results demonstrate that the proposed method yields lower error rates in TD-SV when compared to a set of existing methods. A thorough ablation study further confirms the effectiveness of the method. Fusion in both score and feature levels also shows the complementary nature of the proposed features.