  In order to build language technologies for majority of the languages, it is
important to leverage the resources available in public domain on the internet
- commonly referred to as `Found Data'. However, such data is characterized by
the presence of non-standard, non-trivial variations. For instance, speech
resources found on the internet have non-speech content, such as music.
Therefore, speech recognition and speech synthesis models need to be robust to
such variations. In this work, we present an analysis to show that it is
important to disentangle the latent causal factors of variation in the original
data to accomplish these tasks. Based on this, we present approaches to
disentangle such variations from the data using Latent Stochastic Models.
Specifically, we present a method to split the latent prior space into
continuous representations of dominant speech modes present in the magnitude
spectra of audio signals. We propose a completely unsupervised approach using
multinode latent space variational autoencoders (VAE). We show that the
constraints on the latent space of a VAE can be in-fact used to separate speech
and music, independent of the language of the speech. This paper also
analytically presents the requirement on the number of latent variables for the
task based on distribution of the speech data.
