Robust End-to-End Diarization with Domain Adaptive Training and Multi-Task Learning

Due to the scarcity of publicly available diarization data, the model performance can be improved by training a single model with data from different domains. In this work, we propose to incorporate domain information to train a single end-to-end diarization model for multiple domains. First, we employ domain adaptive training with parameter-efficient adapters for on-the-fly model reconfiguration. Second, we introduce an auxiliary domain classification task to make the diarization model more domain-aware. For seen domains, the combination of our proposed methods reduces the absolute DER from 17.66% to 16.59% when compared with the baseline. During inference, adapters from ground-truth domains are not available for unseen domains. We demonstrate our model exhibits a stronger generalizability to unseen domains when adapters are removed. For two unseen domains, this improves the DER performance from 39.91% to 23.09% and 25.32% to 18.76% over the baseline, respectively.Comment: 7 pages, 2 figures, ASRU 202

EEND-M2F: Masked-attention mask transformers for speaker diarization

In this paper, we make the explicit connection between image segmentation methods and end-to-end diarization methods. From these insights, we propose a novel, fully end-to-end diarization model, EEND-M2F, based on the Mask2Former architecture. Speaker representations are computed in parallel using a stack of transformer decoders, in which irrelevant frames are explicitly masked from the cross attention using predictions from previous layers. EEND-M2F is lightweight, efficient, and truly end-to-end, as it does not require any additional diarization, speaker verification, or segmentation models to run, nor does it require running any clustering algorithms. Our model achieves state-of-the-art performance on several public datasets, such as AMI, AliMeeting and RAMC. Most notably our DER of 16.07% on DIHARD-III is the first major improvement upon the challenge winning system.Comment: 14 pages, 2 figure

FACTORIZED HIDDEN LAYER ADAPTATION FOR DEEP NEURAL NETWORK BASED ACOUSTIC MODELING

Ph.DDOCTOR OF PHILOSOPH

Effectiveness of the pharmacological interventions on abstinence of substance abuse disorder.

The abundance of pharmacological interventions to treat substance abuse disorder has solidified globally. Despite promising effects, use of pharmacological interventions in substance abuse disorder are limited in asian territories. This study aimed to identify and explore existing effective pharmacological interventions on abstinence of substance abuse disorder. A systematic review was conducted adhering to PRISMA guidelines. Google scholar, Pubmed, Hinari, and Cochrane databases were systematically searched and the topic and abstract of the articles were screened for eligibility. Articles of empirical studies on pharmacological interventions on abstinence of substance abuse disorder, which were published in peer-reviewed journals during 2010 to 2020, written in English, were included and articles on alcohol and smoking cessation were excluded from the review. Full papers were then assessed against eligibility criteria. Quality appraisal and data extraction of the selected articles were performed by two independent reviewers and discrepancies were discussed with another independent reviewer to reach consensus. Three hundred and seven research articles were identified through a comprehensive database search. After screening the topics and abstracts of the articles and assessing the relevant full texts for eligibility, 26 articles of the empirical studies were included in the systematic review. High doses of Buprenorphine, Methadone, Lofexidine, Naltrexone, SB-334867, Prazosin, and Baclofen were identified to be significantly effective in abstinence from substance abuse. It was concluded that empirical evidence of effective pharmacological interventions exists and its combination with existing non-pharmacological rehabilitation interventions are proposed as more effective in the treatment of substance abus