Spoken language understanding
AssigneeAdobe Inc.
InventorsNIKITA KAPOOR, Jaya Dodeja, Nikaash Puri
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Abstract
Systems and methods for spoken language understanding are described. Embodiments of the systems and methods receive audio data for a spoken language expression, encode the audio data using a multi-stage encoder comprising a basic encoder and a sequential encoder, wherein the basic encoder is trained to generate character features during a first training phase and the sequential encoder is trained to generate token features during a second training phase, and decode the token features to generate semantic information representing the spoken language expression.
Claims20
A method of spoken language understanding, the method comprising:
- receiving audio data for a spoken language expression;
- encoding the audio data using a multi-stage encoder comprising a basic encoder and a sequential encoder, wherein the basic encoder is trained to generate character features during a first training phase and the sequential encoder is trained to generate token features during a second training phase;
- and
- decoding the token features to generate semantic information representing the spoken language expression.
A method of training a neural network for spoken language understanding, the method comprising:
- training a basic encoder to generate character features based on a spoken language expression;
- training a sequential encoder to generate token features based on the spoken language expression;
- combining the basic encoder, the sequential encoder and a decoder in sequence to produce an end-to-end neural network for spoken language understanding;
- and
- training the end-to-end network to generate semantic information for the spoken language expression.
An apparatus for spoken language understanding, comprising:
- a basic encoder configured to generate character features based on audio data for a spoken language expression, wherein the basic encoder is trained in a first training phase;
- a sequential encoder configured to generate token features based on an output of the basic encoder, wherein the sequential encoder is trained during a second training phase;
- and
- a decoder configured to generate semantic information for the spoken language expression based on an output of the sequential encoder, wherein the decoder is trained together with the basic encoder and the sequential encoder during a third training phase.
Dependencies are inferred from the claim text.