Phonetic aware techniques for Speaker Verification
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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 ...
In air traffic control rooms, paper flight strips are more and more replaced by digital solutions. The digital systems, however, increase the workload for air traffic controllers: For instance, each voice-command must be manually inserted into the system b ...
Feature extraction is a key step in many machine learning and signal processing applications. For speech signals in particular, it is important to derive features that contain both the vocal characteristics of the speaker and the content of the speech. In ...
Although current trends in speech processing consider deep learning through data-driven technologies, many potential applications exhibit lack of training or development data. Therefore, considerably light signal processing techniques are still of interest ...
We show that confidence measures estimated from local posterior probabilities can serve as objective functions for training ANNs in hybrid HMM based speech recognition systems. This leads to a segment-level training paradigm that overcomes the limitation o ...
In this paper, we introduce a novel approach for Language Identification (LID). Two commonly used state-of-the-art methods based on UBM/GMM I-vector technique, combined with a back-end classifier, are first evaluated. The differential factor between these ...
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 ...
Modeling directly raw waveform through neural networks for speech processing is gaining more and more attention. Despite its varied success, a question that remains is: what kind of information are such neural networks capturing or learning for different t ...
Speech-based degree of sleepiness estimation is an emerging research problem. In the literature, this problem has been mainly addressed through modeling of low level of descriptors. This paper investigates an end-to-end approach, where given raw waveform a ...
In Deep Neural Network (DNN) i-vector based speaker recognition systems, acoustic models trained for Automatic Speech Recognition are employed to estimate sufficient statistics for i-vector modeling. The DNN based acoustic model is typically trained on a w ...