Method of setting optimum-partitioned classified neural network and method and apparatus for automatic labeling using optimum-partitioned classified neural network
First Claim
1. A method of automatic labeling to tune a phoneme boundary of a first label file generated by performing automatic labeling of a manual label file, the method comprising:
- searching for neural networks having minimum errors with respect to a number of L phoneme combinations from a number of K neural network combinations generated at an initial stage or updated;
updating weights during learning of the K neural networks by K phoneme combination groups searched with the same neural networks;
composing an optimum-partitioned classified neural network combination using the K neural networks of which a total error sum has converged;
tuning a phoneme boundary of a first label file using a phoneme combination group classification result and the optimum-partitioned classified neural network combination from the composing of the optimum-partitioned classified neural network combination; and
generating a final label file reflecting the tuning result,wherein a phoneme boundary tuning field in the tuning of the phoneme boundary or the first label file and the generating a final label file reflecting the tuning result is set to a predetermined field of a duration time of left and right phonemes of the phoneme combination.
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Abstract
A method of automatic labeling using an optimum-partitioned classified neural network includes searching for neural networks having minimum errors with respect to a number of L phoneme combinations from a number of K neural network combinations generated at an initial stage or updated, updating weights during learning of the K neural networks by K phoneme combination groups searched with the same neural networks, and composing an optimum-partitioned classified neural network combination using the K neural networks of which a total error sum has converged; and tuning a phoneme boundary of a first label file by using the phoneme combination group classification result and the optimum-partitioned classified neural network combination, and generating a final label file reflecting the tuning result.
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Citations
34 Claims
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1. A method of automatic labeling to tune a phoneme boundary of a first label file generated by performing automatic labeling of a manual label file, the method comprising:
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searching for neural networks having minimum errors with respect to a number of L phoneme combinations from a number of K neural network combinations generated at an initial stage or updated; updating weights during learning of the K neural networks by K phoneme combination groups searched with the same neural networks; composing an optimum-partitioned classified neural network combination using the K neural networks of which a total error sum has converged; tuning a phoneme boundary of a first label file using a phoneme combination group classification result and the optimum-partitioned classified neural network combination from the composing of the optimum-partitioned classified neural network combination; and generating a final label file reflecting the tuning result, wherein a phoneme boundary tuning field in the tuning of the phoneme boundary or the first label file and the generating a final label file reflecting the tuning result is set to a predetermined field of a duration time of left and right phonemes of the phoneme combination. - View Dependent Claims (2, 3, 4)
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5. An apparatus for automatic labeling using an optimum-partitioned classified neural network, comprising:
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a labeling unit to generate a first label file by performing automatic labeling for a manual label file; an optimum-partitioned classified neural network composing unit searching neural networks having minimum errors with respect to a number of L phoneme combinations from a number of K neural network combinations generated at an initial stage or updated, updating weights during learning of the K neural networks by K phoneme combination groups searched with the same neural networks, and composing an optimum-partitioned classified neural network combination using the K neural networks of which a total error sum has converged; and a phoneme boundary tuning unit tuning a phoneme boundary of the first label file by using a phoneme combination group classification result and the optimum-partitioned classified neural network combination supplied from the optimum-partitioned classified neural network composing unit, and generating a final label file reflecting the tuning result, wherein the phoneme boundary tuning field of the phoneme boundary tuning unit is set to a predetermined field of a duration time of left and right phonemes of the phoneme combination. - View Dependent Claims (6, 7, 8)
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9. An apparatus for automatic labeling using an optimum-partitioned classified neural network, comprising:
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a labeling unit to perform automatic labeling of a manual label file and generate a first label file; an optimum-partitioned classified neural network composing unit to receive input variables, segment phoneme combinations into partitions applicable to neural networks, and compose optimum-partitioned classified neural networks of Multi-Layer Perceptron-type from re-learned partitions; and a phoneme boundary tuning unit to tune a phoneme boundary of the first label file supplied from the labeling unit and to generate a final label file reflecting the tuning result, wherein the phoneme boundary tuning unit tunes the phoneme boundary by using the optimum-partitioned classified neural networks composed after completing learning in the optimum-partitioned classified neural network composing unit and judges the phoneme boundary according to whether an output of a neural network is 1 or 0 after applying the same input variable as the input variable used during the learning. - View Dependent Claims (10, 11)
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12. A method of composing the optimum neural network combination minimizing a total error sum, comprising:
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preparing an initial neural network combination using input variables; searching for the optimum neural network having minimum errors with respect to the phoneme combinations from the initial neural network combination and classifying phoneme combinations with optimum neural networks; merging phoneme combinations with the same neural network and classifying the merged phoneme combinations into new partitions; updating each neural network and learning each neural network according to the partitions generated by the classifying and the merging; determining whether all neural networks are converged, wherein if all neural networks are converged, composing the neural network combination as an optimum-partitioned classified neural network combination, and if all neural networks are not converged, then re-learning the optimum neural network having minimum errors by repeating the classifying, the merging, and the updating operations until all neural networks are converged. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31)
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32. A method of learning and updating a neural network, comprising:
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preparing initial neural network combinations composing neural networks of multi-layer perceptron-type; searching a multi-layer perceptron index having a minimum error in the initial neural network combinations of all phoneme combinations; classifying merged phoneme combinations into new partitions by merging phoneme combinations with the same multi-layer perceptron index if the multi-layer perceptron index having a minimum error for all phoneme combinations is searched; and re-training neural networks to update weights by learning data applicable to each partition, wherein the re-training procedure of individual neural networks calculates errors using the updated weights and repeating the re-training until a changing rate of errors becomes smaller than a first threshold value. - View Dependent Claims (33, 34)
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Specification