The VOiCES from a Distance Challenge 2019 Evaluation Plan

The "VOiCES from a Distance Challenge 2019" is designed to foster research in the area of speaker recognition and automatic speech recognition (ASR) with the special focus on single channel distant/far-field audio, under noisy conditions. The main objectives of this challenge are to: (i) benchmark state-of-the-art technology in the area of speaker recognition and automatic speech recognition (ASR), (ii) support the development of new ideas and technologies in speaker recognition and ASR, (iii) support new research groups entering the field of distant/far-field speech processing, and (iv) provide a new, publicly available dataset to the community that exhibits realistic distance characteristics.Comment: Special Session for Interspeech 201

Data augmentation versus noise compensation for x- vector speaker recognition systems in noisy environments

The explosion of available speech data and new speaker modeling methods based on deep neural networks (DNN) have given the ability to develop more robust speaker recognition systems. Among DNN speaker modelling techniques, x-vector system has shown a degree of robustness in noisy environments. Previous studies suggest that by increasing the number of speakers in the training data and using data augmentation more robust speaker recognition systems are achievable in noisy environments. In this work, we want to know if explicit noise compensation techniques continue to be effective despite the general noise robustness of these systems. For this study, we will use two different x-vector networks: the first one is trained on Voxceleb1 (Protocol1), and the second one is trained on Voxceleb1+Voxveleb2 (Protocol2). We propose to add a denoising x-vector subsystem before scoring. Experimental results show that, the x-vector system used in Protocol2 is more robust than the other one used Protocol1. Despite this observation we will show that explicit noise compensation gives almost the same EER relative gain in both protocols. For example, in the Protocol2 we have 21% to 66% improvement of EER with denoising techniques

An empirical analysis of information encoded in disentangled neural speaker representations

The primary characteristic of robust speaker representations is that they are invariant to factors of variability not related to speaker identity. Disentanglement of speaker representations is one of the techniques used to improve robustness of speaker representations to both intrinsic factors that are acquired during speech production (e.g., emotion, lexical content) and extrinsic factors that are acquired during signal capture (e.g., channel, noise). Disentanglement in neural speaker representations can be achieved either in a supervised fashion with annotations of the nuisance factors (factors not related to speaker identity) or in an unsupervised fashion without labels of the factors to be removed. In either case it is important to understand the extent to which the various factors of variability are entangled in the representations. In this work, we examine speaker representations with and without unsupervised disentanglement for the amount of information they capture related to a suite of factors. Using classification experiments we provide empirical evidence that disentanglement reduces the information with respect to nuisance factors from speaker representations, while retaining speaker information. This is further validated by speaker verification experiments on the VOiCES corpus in several challenging acoustic conditions. We also show improved robustness in speaker verification tasks using data augmentation during training of disentangled speaker embeddings. Finally, based on our findings, we provide insights into the factors that can be effectively separated using the unsupervised disentanglement technique and discuss potential future directions.Comment: Submitted to Speaker Odyssey 202