System and method for training speech processing neural networks for dynamic loads
AssigneeMicrosoft Technology Licensing, LLC
InventorsFelix Weninger, Marco Gaudesi, Puming Zhan
Life of this patent
Estimating the term…
Abstract
A method, computer program product, and computing system for dividing a speech signal into a plurality of chunks. A first context window is defined with a first period of past context for processing the plurality of chunks with a neural network of a speech processing system. The neural network is trained using the first context window. A second context window is defined with a first period of past context for processing the plurality of chunks with the neural network. The neural network is trained using the second context window.
Claims20
A computer-implemented method, executed on a computing device, for training a neural network with various configurations of constraints to account for different use cases, including use cases involving differences in latency and accuracy, where said differences occur due to different sizes of context windows that are being analyzed by the neural network, said method comprising:
- dividing a speech signal into a plurality of chunks;
- selecting a first chunk from among the plurality of chunks, the first chunk being selected for processing by a neural network of a speech processing system;
- defining a first context window for processing by the neural network, wherein the first context window includes the first chunk and a first number of chunks that precede the first chunk in the plurality of chunks, such that the first context window has a first size, and wherein the first context window is an amount of information that is to be concurrently processed by the neural network;
- training the neural network using the first context window, resulting in the neural network being trained to account for context windows having the first size;
- defining a second context window for processing by the neural network, wherein the second context window also includes the first chunk and a second number of chunks that precede the first chunk in the plurality of chunks, such that the second context window has a second size that is different than the first size;
- training the neural network using the second context window-, resulting in the neural network also being trained to account for context windows having the second size,
- wherein, as a result of the neural network being trained based on context windows having multiple disparate sizes, the neural network is adaptable to various configurations of constraints to account for different use cases involving differences in latency and accuracy, where said differences in latency and accuracy occur due to different sizes of context windows that are being analyzed by the trained neural network;
- and
- after the neural network is trained, causing the neural network to dynamically adjust to changes in processing loads to account for scenarios involving the different use cases.
A computing system that trains a neural network with various configurations of constraints to account for different use cases, including use cases involving differences in latency and accuracy, where said differences occur due to different sizes of context windows that are being analyzed by the neural network, said computing system comprising:
- one or more processors;
- and
- one or more hardware storage devices that store instructions that are executable by the one or more processors to cause the computing system to:
- divide a plurality of utterances into a plurality of chunks;
- select a first chunk from among the plurality of chunks, the first chunk being selected for processing by a neural network of a speech processing system;
- define a first context window for processing by the neural network, wherein the first context window includes the first chunk and a first number of chunks that precede the first chunk in the plurality of chunks, such that the first context window has a first size, and wherein the first context window is an amount of information that is to be concurrently processed by the neural network;
- train the neural network using the first context window, resulting in the neural network being trained to account for context windows having the first size;
- define a second context window for processing by the neural network, wherein the second context window also includes the first chunk and a second number of chunks that precede the first chunk in the plurality of chunks, such that the second context window has a second size that is different than the first size;
- train the neural network using the second context window-, resulting in the neural network also being trained to account for context windows having the second size,
- wherein, as a result of the neural network being trained based on context windows having multiple disparate sizes, the neural network is adaptable to various configurations of constraints to account for different use cases involving differences in latency and accuracy, where said differences in latency and accuracy occur due to different sizes of context windows that are being analyzed by the trained neural network;
- and
- after the neural network is trained, cause the neural network to dynamically adjust to changes in processing loads to account for scenarios involving the different use cases.
One or more hardware storage devices that store instructions that are executable by one or more processors to cause the one or more processors to:
- divide a speech signal into a plurality of chunks;
- select a first chunk from among the plurality of chunks, the first chunk being selected for processing by a neural network of a speech processing system;
- define a first context window for processing by the neural network, wherein the first context window includes the first chunk and a first number of chunks that precede the first chunk in the plurality of chunks, such that the first context window has a first size, and wherein the first context window is an amount of information that is to be concurrently processed by the neural network;
- train the neural network using the first context window, resulting in the neural network being trained to account for context windows having the first size;
- define a second context window for processing by the neural network, wherein the second context window also includes the first chunk and a second number of chunks that precede the first chunk in the plurality of chunks, such that the second context window has a second size that is different than the first size;
- train the neural network using the second context window, resulting in the neural network also being trained to account for context windows having the second size;
- process speech signals at run-time using the trained neural network,
- wherein, as a result of the neural network being trained based on context windows having multiple disparate sizes, the neural network is adaptable to various configurations of constraints to account for different use cases involving differences in latency and accuracy, where said differences in latency and accuracy occur due to different sizes of context windows that are being analyzed by the trained neural network, and
- after the neural network is trained, cause the neural network to dynamically adjust to changes in processing loads to account for scenarios involving the different use cases.
Dependencies are inferred from the claim text.