This work studies the use of deep neural networks (DNNs) to address automatic language
identification (LID). Motivated by their recent success in acoustic modelling, we adapt DNNs …

This work explores the use of Long Short-Term Memory (LSTM) recurrent neural networks
(RNNs) for automatic language identification (LID). The use of RNNs is motivated by their …

This work addresses the use of deep neural networks (DNNs) in automatic language
identification (LID) focused on short test utterances. Motivated by their recent success in …

In the domain of speech recognition many methods have been proposed over time like
Gaussian mixture models (GMM), GMM with universal background model (GMM-UBM …

This paper is aimed at develo** a two-stage language identification (LID) system for
Northeast Indian languages. In the first stage, languages are pre-classified into tonal and …

Automated assessing prosody of oral reading fluency presents challenges due to the
inherent difficulty of quantifying prosody. This study proposed and evaluated an approach …

Prosody comprises aspects of speech that communicate information beyond written words
related to syntax, sentiment, intent, discourse, and comprehension. Decades of research …

This paper presents a two-stage Indian language identification (TS-LID) system which is
made up of a tonal/non-tonal pre-classification and individual language identification …

Dialects are forms of speech, separated from languages which they belong to in terms of
some characteristics and which are specific to a certain region of the country. Obtaining …

In this paper we propose a method to model speaker and session variability and able to
generate likelihood ratios using neural networks in an end-to-end phrase dependent …