We investigate multilingual modeling in the context of a deep neural network (DNN)-hidden
Markov model (HMM) hybrid, where the DNN outputs are used as the HMM state likelihoods …

In this paper we present novel language-independent bottleneck (BN) feature extraction
framework. In our experiments we have used Multilingual Artificial Neural Network (ANN) …

We propose a new technique for training deep neural networks (DNNs) as data-driven
feature front-ends for large vocabulary continuous speech recognition (LVCSR) in low …

Natural language and human–machine interaction is a very much traversed as well as
challenging research domain. However, the main objective is of getting the system that can …

We investigate the use of cross-lingual acoustic data to initialise deep neural network (DNN)
acoustic models by means of unsupervised restricted Boltzmann machine (RBM) pre …

This paper reviews the literature related to the acoustic–phonetic analysis of speech and the
speech recognition approaches that use these types of knowledge. At first, acoustic …

We investigate two strategies to improve the contextdependent deep neural network hidden
Markov model (CDDNN-HMM) in low-resource speech recognition. Although outperforming …

In develo** speech recognition based services for any task domain, it is necessary to
account for the support of an increasing number of languages over the life of the service …

Speech recognition systems normally use handcrafted pronunciation lexicons designed by
linguistic experts. Building and maintaining such a lexicon is expensive and time …

Standard automatic speech recognition (ASR) systems are increasingly expected to
recognize foreign entities, yet doing so while preserving accuracy on native words remains a …