Understanding and Visualizing Raw Waveform-based CNNs
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In the last decade, i-vector and Joint Factor Analysis (JFA) approaches to speaker modeling have become ubiquitous in the area of automatic speaker recognition. Both of these techniques involve the computation of posterior probabilities, using either Gauss ...
The goal of this thesis is to improve current state-of-the-art techniques in speaker verification
(SV), typically based on âidentity-vectorsâ (i-vectors) and deep neural network (DNN), by exploiting diverse (phonetic) information extracted using variou ...
Speaker verification systems traditionally extract and model cepstral features or filter bank energies from the speech signal. In this paper, inspired by the success of neural network-based approaches to model directly raw speech signal for applications su ...
Speech is a complex signal produced by a highly constrained articulation machinery. Neuro and psycholinguistic theories assert that speech can be decomposed into molecules of structured atoms. Although characterization of the atoms is controversial, the ex ...
We address the problem of automatically predicting group performance on a task, using multimodal features derived from the group conversation. These include acoustic features extracted from the speech signal, and linguistic features derived from the conver ...
In a recent work, we have shown that speaker verification systems can be built where both features and classifiers are directly learned from the raw speech signal with convolutional neural networks (CNNs). In this framework, the training phase also decides ...
In air traffic control rooms around the world paper flight strips are replaced through different digital solutions. This enables other systems to access the instructed air traffic controller (ATCo) commands and use them for other purposes. Digital flight s ...
The speech signal conveys information on different time scales from short (20--40 ms) time scale or segmental, associated to phonological and phonetic information to long (150--250 ms) time scale or supra segmental, associated to syllabic and prosodic info ...
Over these last few years, the use of Artificial Neural Networks (ANNs), now often referred to as deep learning or Deep Neural Networks (DNNs), has significantly reshaped research and development in a variety of signal and information processing tasks. Whi ...
This paper explores novel ideas in building end-to-end deep neural network (DNN) based text-dependent speaker verification (SV) system. The baseline approach consists of mapping a variable length speech segment to a fixed dimensional speaker vector by esti ...
