Publication

Exploiting sequence information for text-dependent Speaker Verification

Petr Motlicek, Subhadeep Dey
2017
Conference Papers

Abstract

Model-based approaches to Speaker Verification (SV), such as Joint Factor Analysis (JFA), i-vector and relevance Maximum-a-Posteriori (MAP), have shown to provide state-of-the-art performance for text-dependent systems with fixed phrases. The performance of i-vector and JFA models has been further enhanced by estimating posteriors from Deep Neural Network (DNN) instead of Gaussian Mixture Model (GMM). While both DNNs and GMMs aim at incorporating phonetic information of the phrase with these posteriors, model-based SV approaches ignore the sequence information of the phonetic units of the phrase. In this paper, we tackle this issue by applying dynamic time warping using speaker-informative features. We propose to use i-vectors computed from short segments of each speech utterance, also called online i-vectors, as feature vectors. The proposed approach is evaluated on the RedDots database and provides an improvement of 75% relative equal error rate over the best model-based SV baseline system in a content-mismatch condition.

Official source

https://infoscience.epfl.ch/entities/publication/dc06d160-721a-4aa3-a4e9-903ebeafe72f

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Petr Motlicek, Subhadeep Dey
2017
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/dc06d160-721a-4aa3-a4e9-903ebeafe72f

About this result

Related concepts (31)

Speaker recognition

Speaker recognition is the identification of a person from characteristics of voices. It is used to answer the question "Who is speaking?" The term voice recognition can refer to speaker recognition or speech recognition. Speaker verification (also called speaker authentication) contrasts with identification, and speaker recognition differs from speaker diarisation (recognizing when the same speaker is speaking).

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.

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.

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