13. One or more non-transitory computer-readable media storing executable instructions that, when executed by a processor, cause a system to: receive a sequence of speech features that characterize an unknown speech input provided on an audio input channel controlled by an application executing on the system; receive meta-data that characterizes the audio input channel, an audio codec applied when generating the sequence of speech features, and a type of the application; transform the sequence of speech features using one or more trained mapping functions including a feature-space maximum mutual information (fMMI) mapping function, the one or more trained mapping functions controlled by the meta-data that characterizes the audio input channel, the audio codec applied when generating the sequence of speech features, and the type of the application, the fMMI mapping function using neural network based posterior estimates that use the meta-data as input, wherein transforming the sequence of speech features comprises reducing a dimensionality of the sequence of speech features; and perform automatic speech recognition of the transformed speech features.
13. One or more non-transitory computer-readable media storing executable instructions that, when executed by a processor, cause a system to: receive a sequence of speech features that characterize an unknown speech input provided on an audio input channel controlled by an application executing on the system; receive meta-data that characterizes the audio input channel, an audio codec applied when generating the sequence of speech features, and a type of the application; transform the sequence of speech features using one or more trained mapping functions including a feature-space maximum mutual information (fMMI) mapping function, the one or more trained mapping functions controlled by the meta-data that characterizes the audio input channel, the audio codec applied when generating the sequence of speech features, and the type of the application, the fMMI mapping function using neural network based posterior estimates that use the meta-data as input, wherein transforming the sequence of speech features comprises reducing a dimensionality of the sequence of speech features; and perform automatic speech recognition of the transformed speech features. 15. The one or more non-transitory computer-readable media of claim 13 , wherein the one or more trained mapping functions include a linear discriminant analysis (LDA) mapping function.