Google Inc.

Christopher Walter George Thornton

Tara N Sainath

Michiel A U Bacchiani

Samuel Bengio

Ehsan Variani

Izhak Shafran

Jakob Nicolaus Foerster

Rohit Prakash Prabhavalkar

Mirkó Visontai

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech recognition using complex linear projection are disclosed. In one aspect, a method includes the actions of receiving audio data corresponding to an utterance. The method further includes generating frequency domain data using the audio data. The method further includes processing the frequency domain data using complex linear projection. The method further includes providing the processed frequency domain data to a neural network trained as an acoustic model. The method further includes generating a transcription for the utterance that is determined based at least on output that the neural network provides in response to receiving the processed frequency domain data.

Complex linear projection for acoustic modeling

Linear transformation for speech recognition modeling

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes accessing a first neural network that was trained to recognize a given keyword or keyphrase using a set of hotword training data, wherein the hotword training data includes positive hotword training data that correspond to utterances of the keyword or keyphrase, and negative hotword training data that corresponds to utterances of words or phrases that are other than the keyword or keyphrase, selecting a seed hotsound, mapping, to a feature space, (i) the positive hotword training data, (ii) the negative hotword training data, and (iii) the seed hotsound, performing an optimization of a position of the seed hotsound within the feature space to generate a modified seed hotsound, generating a set of hotsound training data using the modified seed hotsound, training a second neural network to recognize the modified seed hotsound using the generated set of hotsound training data, and using the trained second neural network to recognize the modified hotsound.

Generating sounds for detectability by neural networks

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Mountain View, CA, United States of America

Christopher Walter George Thornton