Publication

Idiap submission to the NIST SRE 2019 Speaker Recognition Evaluation

2019
Reports

Abstract

Idiap has made a submission to the conversational telephony speech (CTS) challenge of the NIST SRE 2019. The submission consists of six speaker verification (SV) systems: four extended TDNN (E-TDNN) and two TDNN x-vector systems. Employment of various training sets, loss functions, adaptation sets and extracted speech features is among the main differences of the submitted systems. Domain adaptation is represented by a supervised method (developed using a limited data) with transfer learning of the batch norm layers. % was applied. Mean shift and covariance estimation of batch norm allows to map the target domain to the source domain, alleviating the problem of over-fitting on the adaptation data. The back-end of all the systems is represented by the conventional Linear Discriminant Analysis (LDA) projection followed by Probabilistic LDA (PLDA) scoring for inference. The PLDA was also adapted unsupervisedly using the unlabelled part of the NIST SRE 2018 set. In addition, training the LDA and PLDA using in-domain data was investigated. The entire system was built around the Kaldi toolkit.

Official source

https://infoscience.epfl.ch/entities/publication/2d2cf1cd-90d0-451f-8a93-73fb63b2ff00

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2019
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/2d2cf1cd-90d0-451f-8a93-73fb63b2ff00

About this result

Related concepts (32)

Convolutional neural network

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.

Speech recognition

Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis.

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

Related publications (32)

Idiap submission to the NIST SRE 2019 Speaker Recognition Evaluation

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