Speech recognition with sequence-to-sequence models

One or more non-transitory machine-readable storage media storing instructions that are executable by one or more processing devices to cause performance of operations comprising:

• receiving audio data for an utterance;
• providing features indicative of acoustic characteristics of the utterance as input to an encoder neural network;
• processing an output of the encoder neural network using an attender neural network to generate a context vector;
• generating speech recognition scores using the context vector and a decoder neural network, wherein the decoder neural network has been trained using a training process that selects at least one input to the decoder neural network with a predetermined probability, wherein the at least one input to the decoder neural network during training is selected between (i) input data based on a known value for an element in a training example, and (ii) input data based on an output of the decoder neural network for the element in the training example;
• generating a transcription for the utterance using word elements selected based on the speech recognition scores;
• and
• providing the transcription as an output of an automated speech recognition system.

An automated speech recognition system comprising:

• one or more processing devices;
• and
• one or more non-transitory machine-readable storage media storing instructions that are executable by the one or more processing devices to cause performance of operations comprising:
• receiving audio data for an utterance;
• providing features indicative of acoustic characteristics of the utterance as input to an encoder neural network;
• processing an output of the encoder neural network using an attender neural network to generate a context vector;
• generating speech recognition scores using the context vector and a decoder neural network, wherein the decoder neural network has been trained using a training process that selects at least one input to the decoder neural network with a predetermined probability, wherein the at least one input to the decoder neural network during training is selected between (i) input data based on a known value for an element in a training example, and (ii) input data based on an output of the decoder neural network for the element in the training example;
• generating a transcription for the utterance using word elements selected based on the speech recognition scores;
• and
• providing the transcription as an output of the automated speech recognition system.