Convolutional neural networks
AssigneeGoogle Inc.
InventorsTara N. Sainath, Maria Carolina Parada San Martin
Life of this patent
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Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for keyword spotting. One of the methods includes training, by a keyword detection system, a convolutional neural network for keyword detection by providing a two-dimensional set of input values to the convolutional neural network, the input values including a first dimension in time and a second dimension in frequency, and performing convolutional multiplication on the two-dimensional set of input values for a filter using a frequency stride greater than one to generate a feature map.
Claims20
A speech recognition model comprising:
- a convolution neural network comprising:
- a first convolution neural network layer configured to generate a first output from a two-dimensional set of input values, the set of input values comprising input values across a first dimension in time and input values across a second dimension in frequency, and the first output comprising a feature map;
- a second convolution neural network layer different than the first convolution neural network layer, the second convolution neural network layer configured to receive the feature map generated by the first convolution neural network layer and generate a second output using the feature map;
- and
- a linear low rank layer configured to receive the second output generated by the second convolution neural network layer and generate a third output using the second output;
- and
- a deep neural network configured to receive the third output generated by the linear low rank layer and generate a fourth output using the third output.
A method for training a speech recognition model, the method comprising:
- generating, by a first layer of a convolution neural network, a first output from a two-dimensional set of input values, the set of input values comprising input values across a first dimension in time and input values across a second dimension in frequency, and the first output comprising a feature map;
- generating, by a second layer of the convolution neural network, a second output using the feature map;
- generating, by a linear low rank layer, a third output using the second output;
- generating, by a deep neural network, a fourth output using the third output;
- and
- generating, by a softmax layer, a final output of the speech recognition model using the fourth output.
Dependencies are inferred from the claim text.