  In most automatic speech recognition (ASR) systems, the audio signal is
processed to produce a time series of sensor measurements (e.g., filterbank
outputs). This time series encodes semantic information in a speaker-dependent
way. An earlier paper showed how to use the sequence of sensor measurements to
derive an "inner" time series that is unaffected by any previous invertible
transformation of the sensor measurements. The current paper considers two or
more speakers, who mimic one another in the following sense: when they say the
same words, they produce sensor states that are invertibly mapped onto one
another. It follows that the inner time series of their utterances must be the
same when they say the same words. In other words, the inner time series
encodes their speech in a manner that is speaker-independent. Consequently, the
ASR training process can be simplified by collecting and labelling the inner
time series of the utterances of just one speaker, instead of training on the
sensor time series of the utterances of a large variety of speakers. A similar
argument suggests that the inner time series of music is
instrument-independent. This is demonstrated in experiments on monophonic
electronic music.
