  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 Recognition. Our work focuses on transcription of
multi-genre broadcast media, which is often only categorised broadly in terms
of high level genres such as sports, news, documentary, etc. However, in terms
of acoustic modelling these categories are coarse. Instead, it is expected that
a mixture of latent domains can better represent the complex and diverse
behaviours within a TV show, and therefore lead to better and more robust
performance. We propose a new method, whereby these latent domains are
discovered with Latent Dirichlet Allocation, in an unsupervised manner. These
are used to adapt DNNs using the Unique Binary Code (UBIC) representation for
the LDA domains. Experiments conducted on a set of BBC TV broadcasts, with more
than 2,000 shows for training and 47 shows for testing, show that the use of
LDA-UBIC DNNs reduces the error up to 13% relative compared to the baseline
hybrid DNN models.
