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 …

This paper proposes two novel frontends for robust language identification (LID) using a
convolutional neural network (CNN) trained for automatic speech recognition (ASR). In the …

In this work, we propose an end-to-end approach to the language identification (LID)
problem based on Convolutional Deep Neural Networks (CDNNs). The use of CDNNs is …

Using bottleneck features extracted from a deep neural network (DNN) trained to predict
senone posteriors has resulted in new, state-of-the-art technology for language and speaker …

This paper presents an alternative feature set to the traditional MFCC-SDC used in acoustic
approaches to Spoken Language Recognition: the log-likelihood ratios of phone posterior …

Sustaining automatic speaker verification (ASV) systems from spoofing attacks remains an
essential challenge, even if significant progress in ASV has been achieved in recent years …

This study proposes a novel deep neural network (DNN) based approach to language
identification (LID) for the NIST 2015 Language Recognition (LRE) i-Vector Machine …

This paper proposes adaptive Gaussian backend (AGB), a novel approach to robust
language identification (LID). In this approach, a given test sample is compared to language …

We describe a language identification system developed for robustess to noise conditions
such as those encountered under the DARPA RATS program, which is focused on multi …

In real scenarios, robust language identification (LID) is usually hindered by factors such as
background noise, channel, and speech duration mismatches. To address these issues, this …