Abstract: There is provided a system and method for processing and/or recognizing acoustic signals. The method comprises obtaining at least one pre-existing speech recognition model; adapting and/or training the at least one pre-existing speech recognition model incrementally when new, previously unseen, user-specific data is received, the data comprising input acoustic signals and/or user action demonstrations and/or semantic information about a meaning of the acoustic signals, wherein the at least one model is incrementally updated by associating new input acoustic signals with input semantic frames to enable recognition of changed input acoustic signals. The method further comprises adapting to a user's vocabulary over time by learning new words and/or removing words no longer being used by the user, generating a semantic frame from an input acoustic signal according to the at least one model, and mapping the semantic frame to a predetermined action.

Abstract: The present disclosure relates to speech recognition systems and methods that enable personalized vocal user interfaces. More specifically, the present disclosure relates to combining a self-learning speech recognition system based on semantics with a speech-to-text system optionally integrated with a natural language processing system. The combined system has the advantage of automatically and continually training the semantics-based speech recognition system and increasing recognition accuracy.

Abstract: The present disclosure relates to speech recognition systems and methods that enable personalized vocal user interfaces. More specifically, the present disclosure relates to combining a self-learning speech recognition system based on semantics with a speech-to-text system optionally integrated with a natural language processing system. The combined system has the advantage of automatically and continually training the semantics-based speech recognition system and increasing recognition accuracy.