Abstract: A system uses a machine learning model trained using multitask learning for processing audio signals of different types. The system may be used in a device such as a headset, smart glasses, or a smart watch, and may be used to make predictions based on different types of audio signals, for example, for classifying acoustic events based on audio signal or for keyword spotting to detect wake words. The use of a single machine learning model for analyzing different types of audio signals improves storage efficiency as well as energy efficiency of devices compared to systems that use a different machine learning model for processing each type of audio signal.

Abstract: A revised support vector machine (SVM) classifier is offered to distinguish between true keywords and false positives based on output from a keyword spotting component of a speech recognition system. The SVM operates on a reduced set of feature dimensions, where the feature dimensions are selected based on their ability to distinguish between true keywords and false positives. Further, support vectors pairs are merged to create a reduced set of re-weighted support vectors. These techniques result in an SVM that may be operated using reduced computing resources, thus improving system performance.