Processing audio waveforms

A method comprising:

• obtaining audio waveform samples from audio data describing an utterance;
• providing the audio waveform samples as input to a trained neural network that has been trained to classify speech,
• wherein the trained neural network comprises a feature extraction portion that comprises at least one layer of the trained neural network,
• wherein the trained neural network comprises a classification portion comprising at least one layer of the trained neural network, and
• wherein the feature extraction portion is configured to (i) generate a plurality of values based on the samples from the audio waveform data using parameters determined through training of the trained neural network, and (ii) generate a feature vector for output to the classification portion that has a reduced dimensionality with respect to the plurality of values;
• receiving output data that the trained neural network generates in response to receiving the audio waveform samples, wherein the output data indicates likelihoods that different speech units have occurred;
• and
• using the output data from the trained neural network to identify one or more words included in the utterance.

A system comprising:

• one or more computers;
• and
• one or more computer-readable media storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
• obtaining audio waveform samples from audio data describing an utterance;
• providing the audio waveform samples as input to a trained neural network that has been trained to classify speech,
• wherein the trained neural network comprises a feature extraction portion that comprises at least one layer of the trained neural network,
• wherein the trained neural network comprises a classification portion comprising at least one layer of the trained neural network, and
• wherein the feature extraction portion is configured to (i) generate a plurality of values based on the samples from the audio waveform data using parameters determined through training of the trained neural network, and (ii) generate a feature vector for output to the classification portion that has a reduced dimensionality with respect to the plurality of values;
• receiving output data that the trained neural network generates in response to receiving the audio waveform samples, wherein the output data indicates likelihoods that different speech units have occurred;
• and
• using the output data from the trained neural network to identify one or more words included in the utterance.

One or more non-transitory computer-readable media storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:

• obtaining audio waveform samples from audio data describing an utterance;
• providing the audio waveform samples as input to a trained neural network that has been trained to classify speech,
• wherein the trained neural network comprises a feature extraction portion that comprises at least one layer of the trained neural network,
• wherein the trained neural network comprises a classification portion comprising at least one layer of the trained neural network, and
• wherein the feature extraction portion is configured to (i) generate a plurality of values based on the samples from the audio waveform data using parameters determined through training of the trained neural network, and (ii) generate a feature vector for output to the classification portion that has a reduced dimensionality with respect to the plurality of values;
• receiving output data that the trained neural network generates in response to receiving the audio waveform samples, wherein the output data indicates likelihoods that different speech units have occurred;
• and
• using the output data from the trained neural network to identify one or more words included in the utterance.