PATENT CLAIM ANALYSIS

Application Number: 15758280
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-09
Patent Classification: ["704", "266000"]

Abstract:
A training method for multiple personalized acoustic models, and a voice synthesis method and device, for voice synthesis. The method comprises: training a reference acoustic model, based on first acoustic feature data of training voice data and first text annotation data corresponding to the training voice data (S 11 ); acquiring voice data of a target user (S 12 ); training a first target user acoustic model according to the reference acoustic model and the voice data (S 13 ); generating second acoustic feature data of the first text annotation data, according to the first target user acoustic model and the first text annotation data (S 14 ); and training a second target user acoustic model, based on the first text annotation data and the second acoustic feature data (S 15 ).

Claim (Index 19):
The method according to  claim 3 , wherein training a second target user acoustic model based on the first text annotation data and the second acoustic feature data, comprises:\n performing training on the first text annotation data and the second acoustic feature data based on a hidden Markov model, and building the second target user acoustic model according to a result of the training.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 90.0
- Lexical Diversity: 3.23913
- Patent Class: 704.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['15758214', '11549412', '10329181', '10355296', '12639164']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3499914164703191
- 35 USC 102 Novelty (BERT): 0.536523955313866
- Combined Prediction Score: 0.3686446703546738
- Mean Citation Score: 349.3658660000001
- Max Citation Score: 391.96466
- Similarity Product: 252.53256783866047

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test