Generalized domain adaptation framework for parametric back-end in speaker recognition
State-of-the-art speaker recognition systems comprise a speaker embedding front-end followed by a probabilistic linear discriminant analysis (PLDA) back-end. The effectiveness of these components relies on the availability of a large amount of labeled training data. In practice, it is common for domains (e.g., language, channel, demographic) in which a system is deployed to differ from that in which a system has been trained. To close the resulting gap, domain adaptation is often essential for PLDA models. Among two of its variants are Heavy-tailed PLDA (HT-PLDA) and Gaussian PLDA (G-PLDA). Though the former better fits real feature spaces than does the latter, its popularity has been severely limited by its computational complexity and, especially, by the difficulty, it presents in domain adaptation, which results from its non-Gaussian property. Various domain adaptation methods have been proposed for G-PLDA. This paper proposes a generalized framework for domain adaptation that can be applied to both of the above variants of PLDA for speaker recognition. It not only includes several existing supervised and unsupervised domain adaptation methods but also makes possible more flexible usage of available data in different domains. In particular, we introduce here two new techniques: (1) correlation-alignment in the model level, and (2) covariance regularization. To the best of our knowledge, this is the first proposed application of such techniques for domain adaptation w.r.t. HT-PLDA. The efficacy of the proposed techniques has been experimentally validated on NIST 2016, 2018, and 2019 Speaker Recognition Evaluation (SRE'16, SRE'18, and SRE'19) datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain AdaptationSpeaker RecognitionUnsupervised Domain AdaptationSimilar Papers 제목 키워드 기반
Dynamically Instance-Guided Adaptation: A Backward-Free Approach for Test-Time Domain Adaptive Semantic Segmentation
In this paper, we study the application of Test-time domain adaptation in semantic segmentation (TTDA-Seg) where both efficiency and effectiveness are crucial. Existing methods either have low efficiency (e.g., backw…
Domain AdaptationSemantic SegmentationTowards Backwards-Compatible Data with Confounded Domain Adaptation
Most current domain adaptation methods address either covariate shift or label shift, but are not applicable where they occur simultaneously and are confounded with each other. Domain adaptation approaches which do accou…
Domain AdaptationNeural Distribution Learning for generalized time-to-event prediction
Predicting the time to the next event is an important task in various domains. However, due to censoring and irregularly sampled sequences, time-to-event prediction has resulted in limited success only for particular ta…
PredictionProbabilistic ProgrammingTime-to-Event PredictionA Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation
We introduce a new algorithm for multi-objective reinforcement learning (MORL) with linear preferences, with the goal of enabling few-shot adaptation to new tasks. In MORL, the aim is to learn policies over multiple comp…
Multi-Objective Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders
Domain adaptation approaches aim to exploit useful information from the source domain where supervised learning examples are easier to obtain to address a learning problem in the target domain where there is no or limite…
Domain AdaptationGeneralized Zero-Shot LearningZero-Shot Learning