System and method for speech recognition using deep recurrent neural networks
AssigneeGoogle Inc.
InventorAlexander B. Graves
Life of this patent
Estimating the term…
Abstract
Deep recurrent neural networks applied to speech recognition. The deep recurrent neural networks (RNNs) are preferably implemented by stacked long short-term memory bidirectional RNNs. The RNNs are trained using end-to-end training with suitable regularisation.
Claims20
A system for training
- a deep recurrent neural network (“deep RNN”) to perform speech recognition, the deep RNN being an RNN comprising an input layer, an output layer, and multiple recurrent hidden layers organized in a stack on top of each other;
- wherein the multiple recurrent hidden layers comprise:
- a lowest hidden layer configured to receive input vectors from the input layer, and
- a highest hidden layer configured to generate output vectors received as inputs by the output layer, wherein at least one of the multiple recurrent hidden layers is implemented by a Long Short-term Memory (“LSTM”) RNN, wherein the system comprises one or more computers storing instructions that when executed by the one or more computers cause one or more computers to perform operations comprising:
- receiving training data comprising a plurality of sequences of audio observations and, for each sequence of audio observations, a corresponding sequence of text symbols that represents the sequence of audio observations;
- and
- training the deep RNN to map the sequences of audio observations to the corresponding sequences of text symbols.
A method for training a deep recurrent neural network (“deep RNN”) to perform speech recognition, the deep RNN being an RNN comprising an input layer, an output layer, and multiple recurrent hidden layers organized in a stack on top of each other;
- wherein the multiple recurrent hidden layers comprise:
- a lowest hidden layer configured to receive input vectors from the input layer, and
- a highest hidden layer configured to generate output vectors received as inputs by the output layer, wherein at least one of the multiple recurrent hidden layers is implemented by a Long Short-term Memory (“LSTM”) RNN, wherein the method comprises:
- receiving training data comprising a plurality of sequences of audio observations and, for each sequence of audio observations, a corresponding sequence of text symbols that represents the sequence of audio observations;
- and
- training the deep RNN to map the sequences of audio observations to the corresponding sequences of text symbols.
One or more non-transitory computer-readable storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations for training a deep recurrent neural network (“deep RNN”) to perform speech recognition, the deep RNN being an RNN comprising an input layer, an output layer, and multiple recurrent hidden layers organized in a stack on top of each other;
- wherein the multiple recurrent hidden layers comprise:
- a lowest hidden layer configured to receive input vectors from the input layer, and
- a highest hidden layer configured to generate output vectors received as inputs by the output layer, wherein at least one of the multiple recurrent hidden layers is implemented by a Long Short-term Memory (“LSTM”) RNN, wherein the operations comprise:
- receiving training data comprising a plurality of sequences of audio observations and, for each sequence of audio observations, a corresponding sequence of text symbols that represents the sequence of audio observations;
- and
- training the deep RNN to map the sequences of audio observations to the corresponding sequences of text symbols.
Dependencies are inferred from the claim text.