Method for Automated Training of a Plurality of Artificial Neural Networks
First Claim
1. A computer implemented method, operational on at least one processor, for automated training of a plurality of artificial neural networks for phoneme recognition using training data, wherein the training data comprises speech signals subdivided into frames, each frame associated with a phoneme label, wherein the phoneme label indicates a phoneme associated with the frame, the method comprising:
- a computer process for providing a sequence of frames from the training data, wherein the number of frames in the sequence of frames is at least equal to the number of artificial neural networks;
a computer process for assigning to each of the artificial neural networks a different subsequence of the provided sequence, wherein each subsequence comprises a predetermined number of frames;
a computer process for determining a common phoneme label for the sequence of frames based on the phoneme labels of one or more frames of one or more subsequences of the provided sequence; and
a computer process for training each artificial neural network using the common phoneme label.
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Accused Products
Abstract
The invention provides a method for automated training of a plurality of artificial neural networks for phoneme recognition using training data, wherein the training data comprises speech signals subdivided into frames, each frame associated with a phoneme label, wherein the phoneme label indicates a phoneme associated with the frame. A sequence of frames from the training data are provided, wherein the number of frames in the sequence of frames is at least equal to the number of artificial neural networks. Each of the artificial neural networks is assigned a different subsequence of the provided sequence, wherein each subsequence comprises a predetermined number of frames. A common phoneme label for the sequence of frames is determined based on the phoneme labels of one or more frames of one or more subsequences of the provided sequence. Each artificial neural network using the common phoneme label.
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Citations
28 Claims
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1. A computer implemented method, operational on at least one processor, for automated training of a plurality of artificial neural networks for phoneme recognition using training data, wherein the training data comprises speech signals subdivided into frames, each frame associated with a phoneme label, wherein the phoneme label indicates a phoneme associated with the frame, the method comprising:
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a computer process for providing a sequence of frames from the training data, wherein the number of frames in the sequence of frames is at least equal to the number of artificial neural networks; a computer process for assigning to each of the artificial neural networks a different subsequence of the provided sequence, wherein each subsequence comprises a predetermined number of frames; a computer process for determining a common phoneme label for the sequence of frames based on the phoneme labels of one or more frames of one or more subsequences of the provided sequence; and a computer process for training each artificial neural network using the common phoneme label. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A computer program product including a computer readable storage medium having computer executable code thereon for automated training of a plurality of artificial neural networks for phoneme recognition using training data, wherein the training data comprises speech signals subdivided into frames, each frame associated with a phoneme label, wherein the phoneme label indicates a phoneme associated with the frame, the computer code comprising:
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computer code for providing a sequence of frames from the training data, wherein the number of frames in the sequence of frames is at least equal to the number of artificial neural networks; computer code for assigning to each of the artificial neural networks a different subsequence of the provided sequence, wherein each subsequence comprises a predetermined number of frames; computer code for determining a common phoneme label for the sequence of frames based on the phoneme labels of one or more frames of one or more subsequences of the provided sequence; and computer code for training each artificial neural network using the common phoneme label. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28)
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Specification