Abstract: The present disclosure relates to speech recognition systems and methods using an adaptive incremental learning approach. More specifically, the present disclosure relates to adaptive incremental learning in a self-taught vocal user interface.

Abstract: An automatic speech recognition system includes a recognition engine, a compound lexicon, and a compounder. The recognition engine generates a recognition result having a sequence of recognized words representative of an input utterance. The words in the recognition result include compound word components which may be combined to form compound words. The engine uses a recognition vocabulary of words and a language model which, for a given position in the sequence of recognized words and for selected words in the recognition vocabulary, associates a likelihood of such word occurring at such position. The compound lexicon contains a plurality of compound word components and component connecting links structured so that links are present between components that are more likely to occur together than to occur as separate words.

Abstract: A method and apparatus for removing convolution noise from a signal such a one carrying speech information. The signal is transformed into a log-spectral domain where a smoothed model is fitted to the log-spectrum subject to constraints of concavity and an overall bandpass shape. The smoothed model has quadratic segments of negative curvature and linear segments, the segments being smoothly joined at breakpoints. The model, which may be recursively updated, is subtracted from each log-spectral data vector.