PATENT CLAIM ANALYSIS

Application Number: 15905728
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-07
Patent Classification: ["704", "260000"]

Abstract:
A system and method are presented for outlier identification to remove poor alignments in speech synthesis. The quality of the output of a text-to-speech system directly depends on the accuracy of alignments of a speech utterance. The identification of mis-alignments and mis-pronunciations from automated alignments may be made based on fundamental frequency methods and group delay based outlier methods. The identification of these outliers allows for their removal, which improves the synthesis quality of the text-to-speech system.

Claim (Index 18):
The method of  claim 15 , wherein the identifying of outlying results, applying fundamental frequency, in audio files comprises the steps of:\n a. extracting values of the fundamental frequencies from the audio files; b. generating alignments using the extracted values from the audio files; c. separating out instances of phonemes; d. determining, for each separated instance, an average fundamental frequency value and an average duration value; e. identifying an instance as an outlier, wherein an outlier is identified if:\n i. the phoneme is a vowel; \n ii. the average fundamental frequency of an instance is less than a predetermined value; \n iii. the duration of the instance is greater than twice the average duration of a phoneme; and \n iv. the duration of the instance is less than half of the average duration of a phoneme; \n f. identifying a sum of outliers for each sentence in the audio files, wherein if the sentence has more than a number of outliers, discarding the sentence in the audio files from model training.

Metadata:
- Claim Count in Document: 47.0
- Percentile: 88.0
- Lexical Diversity: 1.64
- Patent Class: 704.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14737080', '13959171', '15850106', '14069492', '14069510']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.312888423462015
- 35 USC 102 Novelty (BERT): 0.6125671997732207
- Combined Prediction Score: 0.3428563010931356
- Mean Citation Score: 342.299508
- Max Citation Score: 599.50775
- Similarity Product: 488.15107473628217

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 0

Dataset: test