This paper investigates data augmentation for deep neural network acoustic modeling
based on label-preserving transformations to deal with data sparsity. Two data …

Spoken content in languages of emerging importance needs to be searchable to provide
access to the underlying information. In this paper, we investigate the problem of extending …

Automatic speech recognition is a core component of many applications, including keyword
search. In this paper we describe experiments on acoustic modeling, language modeling …

We present a system for keyword search on Cantonese conversational telephony audio,
collected for the IARPA Babel program, that achieves good performance by combining …

In order to model nonstationary real‐world processes one can find appropriate theoretical
model with properties following the analyzed data. However in this case many trajectories of …

An overview is made on the ensemble learning efforts that have emerged in automatic
speech recognition in recent years. The approaches that are based on different machine …

This paper is focused on several techniques that improve deep neural network (DNN)
acoustic modeling for audio corpus indexing in the context of the IARPA Babel program …

In this paper we focus on several techniques that improve deep neural network (DNN)
acoustic modeling for low-resource languages. We explore the use of different features such …

Nowadays Human Computer Interaction (HCI) can also be achieved with voice user
interfaces (VUIs). To enable devices to communicate with humans by speech in the user's …

Building accurate acoustic models for low resource languages is the focus of this paper.
Acoustic models are likely to be accurate provided the phone boundaries are determined …