10 citations · 33 across the 5 of their papers we have counts for
8 papers
Latent Dirichlet Allocation Based Acoustic Data Selection for Automatic Speech Recognition
Mortaza, Doulaty, Thomas Hain
Selecting in-domain data from a large pool of diverse and out-of-domain data is a non-trivial problem. In most cases simply using all of the available data will lead to sub-optimal…
Automatic Genre and Show Identification of Broadcast Media
Mortaza Doulaty, Oscar Saz, Raymond W. M. Ng +1
Huge amounts of digital videos are being produced and broadcast every day, leading to giant media archives. Effective techniques are needed to make such data accessible further. Au…
The 2015 Sheffield System for Transcription of Multi-Genre Broadcast Media
Oscar Saz, Mortaza Doulaty, Salil Deena +5
We describe the University of Sheffield system for participation in the 2015 Multi-Genre Broadcast (MGB) challenge task of transcribing multi-genre broadcast shows. Transcription w…
Latent Dirichlet Allocation Based Organisation of Broadcast Media Archives for Deep Neural Network Adaptation
Mortaza Doulaty, Oscar Saz, Raymond W. M. Ng +1
This paper presents a new method for the discovery of latent domains in diverse speech data, for the use of adaptation of Deep Neural Networks (DNNs) for Automatic Speech Recogniti…
Background-tracking Acoustic Features for Genre Identification of Broadcast Shows
Oscar Saz, Mortaza Doulaty, Thomas Hain
This paper presents a novel method for extracting acoustic features that characterise the background environment in audio recordings. These features are based on the output of an a…
The USFD Spoken Language Translation System for IWSLT 2014
Raymond W. M. Ng, Mortaza Doulaty, Rama Doddipatla +7
The University of Sheffield (USFD) participated in the International Workshop for Spoken Language Translation (IWSLT) in 2014. In this paper, we will introduce the USFD SLT system…
Unsupervised Domain Discovery using Latent Dirichlet Allocation for Acoustic Modelling in Speech Recognition
Mortaza Doulaty, Oscar Saz, Thomas Hain
Speech recognition systems are often highly domain dependent, a fact widely reported in the literature. However the concept of domain is complex and not bound to clear criteria. He…
Data-selective Transfer Learning for Multi-Domain Speech Recognition
Mortaza Doulaty, Oscar Saz, Thomas Hain
Negative transfer in training of acoustic models for automatic speech recognition has been reported in several contexts such as domain change or speaker characteristics. This paper…