7. The method of claim 1 , wherein processing the duplicate copy of the sequence comprises compensating for supra-phonetic acoustic properties of the input audio training signal by normalizing the feature vectors of the sequence using at least one of Spectral Subtraction, Vocal Tract Length Normalization, Constrained Maximum Likelihood Regression, Feature Space Maximum Likelihood Regression, Speaker Adaptive Training, Cluster Adaptive Training, or Feature Space Minimum Phone Error.
1. A method comprising: transforming an input audio training signal, using one or more processors of a system, into a sequence of feature vectors, each feature vector of the sequence bearing quantitative measures of acoustic properties of the input audio training signal; processing the sequence of feature vectors with an auto-encoder implemented by the one or more processors to generate (i) an encoded form of the quantitative measures, and (ii) a recovered form of the quantitative measures based on an inverse operation by the auto-encoder on the encoded form of the quantitative measures; processing a duplicate copy of the sequence of feature vectors with a normalizer implemented by the one or more processors to generate a normalized form of the quantitative measures in which supra-phonetic acoustic properties of the input audio training signal are reduced in comparison with phonetic acoustic properties of the input audio training signal; determining an error signal based on a difference between the normalized form of the quantitative measures and the recovered form of the quantitative measures; providing the error signal to the auto-encoder; and by adjusting parameters of the auto-encoder to reduce the magnitude of the error signal, training the auto-encoder to compensate for supra-phonetic acoustic properties of input audio signals.