Hyper-structure recurrent neural networks for text-to-speech
First Claim
1. A method for converting text to speech, the method comprising:
- receiving text input into a plurality of first level recurrent neural networks;
determining, by a first recurrent neural network in the plurality of first level recurrent neural networks, one or more properties of the text input from the group consisting of;
part-of-speech properties, phonemes, linguistic prosody properties, contextual properties, and semantic properties;
determining, by a second recurrent neural network in the plurality of first level recurrent neural networks, one or more properties of the text input from the group consisting of;
part-of-speech properties, phonemes, linguistic prosody properties, contextual properties, and semantic properties, wherein the determined one or properties by the second recurrent neural network is different from the determined one or more properties by the first recurrent neural network;
receiving, by a recurrent neural network in a second level, the determined properties from the first recurrent neural network in the plurality of first level recurrent neural networks and the second recurrent neural network in the plurality of first level recurrent neural networks;
determining by the recurrent neural network in the second level, phonetic properties for the text input based on the properties received from the first recurrent neural network in the plurality of first level recurrent neural networks and the second neural network in the plurality of first level recurrent neural networks, wherein the recurrent neural network in the second level is different from the first recurrent neural network in the plurality of first level recurrent neural networks and the second recurrent neural network in the plurality of first level recurrent neural networks; and
based on the determined phonetic properties, generating a generation sequence for synthetization by an audio synthesizer.
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Abstract
The technology relates to converting text to speech utilizing recurrent neural networks (RNNs). The recurrent neural networks may be implemented as multiple modules for determining properties of the text. In embodiments, a part-of-speech RNN module, letter-to-sound RNN module, a linguistic prosody tagger RNN module, and a context awareness and semantic mining RNN module may all be utilized. The properties from the RNN modules are processed by a hyper-structure RNN module that determine the phonetic properties of the input text based on the outputs of the other RNN modules. The hyper-structure RNN module may generate a generation sequence that is capable of being converting to audible speech by a speech synthesizer. The generation sequence may also be optimized by a global optimization module prior to being synthesized into audible speech.
132 Citations
20 Claims
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1. A method for converting text to speech, the method comprising:
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receiving text input into a plurality of first level recurrent neural networks; determining, by a first recurrent neural network in the plurality of first level recurrent neural networks, one or more properties of the text input from the group consisting of;
part-of-speech properties, phonemes, linguistic prosody properties, contextual properties, and semantic properties;determining, by a second recurrent neural network in the plurality of first level recurrent neural networks, one or more properties of the text input from the group consisting of;
part-of-speech properties, phonemes, linguistic prosody properties, contextual properties, and semantic properties, wherein the determined one or properties by the second recurrent neural network is different from the determined one or more properties by the first recurrent neural network;receiving, by a recurrent neural network in a second level, the determined properties from the first recurrent neural network in the plurality of first level recurrent neural networks and the second recurrent neural network in the plurality of first level recurrent neural networks; determining by the recurrent neural network in the second level, phonetic properties for the text input based on the properties received from the first recurrent neural network in the plurality of first level recurrent neural networks and the second neural network in the plurality of first level recurrent neural networks, wherein the recurrent neural network in the second level is different from the first recurrent neural network in the plurality of first level recurrent neural networks and the second recurrent neural network in the plurality of first level recurrent neural networks; and based on the determined phonetic properties, generating a generation sequence for synthetization by an audio synthesizer. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer storage device, having computer-executable instructions that, when executed by at least one processor, perform a method for converting text-to-speech, the method comprising:
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receiving text input into a plurality of first level recurrent neural networks; determining, by a first recurrent neural network in the plurality of first level recurrent neural networks, one or more properties of the text input from the group consisting of;
part-of-speech properties, phonemes, linguistic prosody properties, contextual properties, and semantic properties;determining, by a second recurrent neural network in the plurality of first level recurrent neural networks, one or more properties of the text input from the group consisting of;
part-of-speech properties, phonemes, linguistic prosody properties, contextual properties, and semantic properties, wherein the determined one or properties by the second recurrent neural network is different from the determined one or more properties by the first recurrent neural network;receiving, by a recurrent neural network in a second level, the determined properties from the first recurrent neural network in the plurality of first level recurrent neural networks and the second recurrent neural networks in the plurality of first level recurrent neural networks; determining by the recurrent neural network in the second level, phonetic properties for the text input based on the properties received from the first recurrent neural network in the plurality of first level recurrent neural networks and the second neural network in the plurality of first level recurrent neural networks, wherein the recurrent neural network in the second level is different from the first recurrent neural network in the plurality of first level recurrent neural networks and the second recurrent neural network in the plurality of first level recurrent neural networks; and based on the determined phonetic properties, generating a generation sequence for synthetization by an audio synthesizer. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
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20. A system for converting text-to-speech comprising:
- at least one processor; and
memory encoding computer executable instructions that, when executed by at least one processor, perform a method for converting text to speech, the method comprising; receiving text input into a plurality of first level recurrent neural networks; determining, by a first recurrent neural network in the plurality of first level recurrent neural networks, one or more properties of the text input from the group consisting of;
part-of-speech properties, phonemes, linguistic prosody properties, contextual properties, and semantic properties;determining, by a second recurrent neural network in the plurality of first level recurrent neural networks, one or more properties of the text input from the group consisting of;
part-of-speech properties, phonemes, linguistic prosody properties, contextual properties, and semantic properties, wherein the determined one or properties by the second recurrent neural network is different from the determined one or more properties by the first recurrent neural network;receiving, by a recurrent neural network in a second level, the determined properties from the first recurrent neural network in the plurality of first level recurrent neural networks and the second recurrent neural network in the plurality of first level recurrent neural networks; determining by the recurrent neural network in the second level, phonetic properties for the text input based on the properties received from the first recurrent neural network in the plurality of first level recurrent neural networks and second neural network in the plurality of first level recurrent neural networks wherein the recurrent neural network in the second level is different from the first recurrent neural network in the plurality of first level recurrent neural networks and the second recurrent neural network in the plurality of first level recurrent neural networks; and based on the determined phonetic properties, generating a generation sequence for synthetization by an audio synthesizer.
- at least one processor; and
Specification