The impressive gains in performance obtained using deep neural networks (DNNs) for
automatic speech recognition (ASR) have motivated the application of DNNs to other …

Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) have recently
outperformed other state-of-the-art approaches, such as i-vector and Deep Neural Networks …

Learned feature representations and sub-phoneme posteriors from Deep Neural Networks
(DNNs) have been used separately to produce significant performance gains for speaker …

Todays interactive devices such as smart-phone assistants and smart speakers often deal
with short-duration speech segments. As a result, speaker recognition systems integrated …

Automatic spoken language identification (LID) is a very important research field in the era of
multilingual voice-command-based human-computer interaction. A front-end LID module …

The recent application of deep neural networks (DNN) to speaker identification (SID) has
resulted in significant improvements over current state-of-the-art on telephone speech. In …

This work compares the performance of deep Locally-Connected Networks (LCN) and
Convolutional Neural Networks (CNN) for text-dependent speaker recognition. These …

This paper compares different approaches for using deep neural networks (DNNs) trained to
predict senone posteriors for the task of spoken language recognition (SLR). These …

Although researchers have proposed numerous techniques for speech emotion recognition,
its performance remains unsatisfactory in many application scenarios. In this study, we …

Automatic language identification has always been a challenging issue and an important
research area in speech signal processing. It is the process of identifying a language from a …