LEARNING SPEECH MODELS FOR MOBILE DEVICE USERS

摘要

Techniques are provided to recognize a speaker's voice. In one embodiment, received audio data may be separated into a plurality of signals. For each signal, the signal may be associated with value/s for one or more features (e.g., Mel-Frequency Cepstral coefficients). The received data may be clustered (e.g., by clustering features associated with the signals). A predominate voice cluster may be identified and associated with a user. A speech model (e.g., a Gaussian Mixture Model or Hidden Markov Model) may be trained based on data associated with the predominate cluster. A received audio signal may then be processed using the speech model to, e.g.,: determine who was speaking; determine whether the user was speaking; determining whether anyone was speaking; and/or determine what words were said. A context of the device or the user may then be inferred based at least partly on the processed signal.