Publication

DNN based speaker embedding using content information for text-dependent speaker verification

2018
Conference Papers

Abstract

In this paper, we are interested in exploring Deep Neural Network (DNN) based speaker embedding for Random-digit task using content information. To this end, a technique is applied to automatically select common phonetic units between the enrollment and test data to produce speaker verification scores. Furthermore, a novel approach is proposed to incorporate content information in the DNN directly. It is hypothesized that features extracted using this DNN will be helpful for the task. Experiments on the RSR dataset show that the proposed method outperforms the baseline i-vector system by 43% relative equal error rate.

Official source

https://infoscience.epfl.ch/entities/publication/9d8210ce-b4b1-4fa4-983b-ad96c6a99141

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2018
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/9d8210ce-b4b1-4fa4-983b-ad96c6a99141

About this result

Related concepts (32)

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.

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.

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.

Related publications (32)

DNN based speaker embedding using content information for text-dependent speaker verification

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