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

Disentangling Speech and Non-Speech Components for Building Robust Acoustic Models from Found Data