Duration prediction modeling in speech synthesis
First Claim
1. A method for training a duration prediction model, comprising:
- generating an initial duration prediction model with a plurality of attributes related to duration prediction and at least part of possible attribute combinations of said plurality of attributes, in which each of said plurality of attributes and said attribute combinations is included as an item;
calculating importance of each item in said duration prediction model with a computer processor;
deleting the item having a lowest importance calculated;
re-generating a duration prediction model with remaining items;
determining whether said re-generated duration prediction model is an optimal model; and
repeating said step of calculating importance and the steps following said step of calculating importance with a newly re-generated duration prediction model, if said duration prediction model is determined as not an optimal model.
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Abstract
The present invention provides a method and apparatus for training a duration prediction model, method and apparatus for duration prediction, method and apparatus for speech synthesis. Said method for training a duration prediction model, comprising: generating an initial duration prediction model with a plurality of attributes related to duration prediction and at least part of possible attribute combinations of said plurality of attributes, in which each of said plurality of attributes and said attribute combinations is included as an item; calculating importance of each said item in said duration prediction model; deleting the item having the lowest importance calculated; re-generating a duration prediction model with the remaining items; determining whether said re-generated duration prediction model is an optimal model; and repeating said step of calculating importance and the following steps, if said duration prediction model is determined as not optimal model.
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Citations
22 Claims
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1. A method for training a duration prediction model, comprising:
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generating an initial duration prediction model with a plurality of attributes related to duration prediction and at least part of possible attribute combinations of said plurality of attributes, in which each of said plurality of attributes and said attribute combinations is included as an item; calculating importance of each item in said duration prediction model with a computer processor; deleting the item having a lowest importance calculated; re-generating a duration prediction model with remaining items; determining whether said re-generated duration prediction model is an optimal model; and repeating said step of calculating importance and the steps following said step of calculating importance with a newly re-generated duration prediction model, if said duration prediction model is determined as not an optimal model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. An apparatus for training a duration prediction model, comprising:
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an initial model generator configured to generate an initial duration prediction model with a plurality of attributes related to duration prediction and at least part of possible attribute combinations of said plurality of attributes, in which each of said plurality of attributes and said attribute combinations is included as an item; an importance calculator configured to calculate importance of each said item in said duration prediction model; an item deleting unit configured to delete the item having a lowest importance calculated; a model re-generator configured to re-generate a duration prediction model with remaining items after a deletion of said item deleting unit; and an optimization determining unit configured to determine whether said duration prediction model re-generated by said model re-generator is an optimal model. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22)
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Specification