Dynamic combination of acoustic model states
AssigneeMicrosoft Technology Licensing, LLC
InventorsKshitiz Kumar, Yifan Gong
Life of this patent
Estimating the term…
Abstract
A computer implemented method classifies an input corresponding to multiple different kinds of input. The method includes obtaining a set of features from the input, providing the set of features to multiple different models to generate state predictions, generating a set of state-dependent predicted weights, and combining the state predictions from the multiple models, based on the state-dependent predicted weights for classification of the set of features.
Claims11
A computer-implemented method for classification of input corresponding to multiple different kinds of input, the method comprising:
- obtaining a set of features from the input, wherein the input comprises speech and the set of features comprise digital representations of speech;
- providing the set of features to multiple different acoustic models that have been trained on different kinds of input, or have different structures or modeling technologies, to generate state predictions, wherein the acoustic models comprise hidden layer outputs;
- generating a set of state-dependent predicted weights from the hidden layer outputs;
- combining the state predictions from the multiple acoustic models, based on the state-dependent predicted weights, for classification of the set of features;
- and providing the combined state predictions to a speech decoder to classify the input as one or more spoken words;
- wherein the set of state-dependent predicted weights are dynamic weights;
- and wherein the hidden layer outputs are concatenated hidden layer outputs that are generated by providing the hidden layer outputs from the different acoustic models to a concatenation function.
A machine-readable storage device having instructions for execution by a processor of a machine to cause the processor to perform operations to perform a method of classifying different kinds of input, the operations comprising:
- obtaining a set of features from the input, wherein the input comprises speech and the set of features comprise digital representations of speech;
- providing the set of features to multiple different acoustic models that have been trained on different kinds of input, or have different structures or modeling technologies, to generate state predictions, wherein the acoustic models comprise hidden layer outputs;
- generating a set of state-dependent predicted weights from the hidden layer outputs;
- combining the state predictions from the multiple acoustic models, based on the state-dependent predicted weights for classification of the set of features;
- and providing the combined state predictions to a speech decoder to classify the input as one or more spoken words;
- wherein the set of state-dependent predicted weights are dynamic weights;
- and wherein the hidden layer outputs are concatenated hidden layer outputs that are generated by providing the hidden layer outputs from the different acoustic models to a concatenation function.
A device comprising:
- a processor;
- and
- a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operations comprising:
- obtaining a set of features from the input, wherein the input comprises speech and the set of features comprise digital representations of speech;
- providing the set of features to multiple different acoustic models that have been trained on different kinds of input, or have different structures or modeling technologies, to generate state predictions, wherein the acoustic models comprise hidden layer outputs;
- generating a set of state-dependent predicted weights from the hidden layer outputs;
- combining the state predictions from the multiple acoustic models, based on the state-dependent predicted weights for classification of the set of features;
- and providing the combined state predictions to a speech decoder to classify the input as one or more spoken words;
- wherein the set of state-dependent predicted weight are dynamic weights;
- wherein the hidden layer outputs ae concatenated hidden layer outputs that are generated by providing the hidden layer outputs from the different acoustic models to a concatenation function.
Dependencies are inferred from the claim text.