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 9):
A method for identifying outlying results in audio files used for model training, in a text-to-speech system, applying group delay algorithms, the method comprising the steps of:\n a. generating alignments of the audio files at a phoneme level; b. generating alignments of the audio files at a syllable level; c. adjusting the alignments at the syllable level using group delay algorithms; d. separating each syllable from the audio files into a separate audio file; e. generating, for each separate audio file, phonemes of the separate audio files using phoneme boundaries for each syllable and an existing phoneme model; f. determining a likelihood value of each generated phoneme, wherein if the likelihood value meets a criteria, identifying the generated phoneme as an outlier; and g. 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 from model training.

Metadata:
- Claim Count in Document: 47.0
- Percentile: 88.0
- Lexical Diversity: 1.64
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14737080', '13959171', '15850106', '14069492', '14069510']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2846809609960523
- 35 USC 102 Novelty (BERT): 0.620238474602206
- Combined Prediction Score: 0.3182367123566677
- Mean Citation Score: 342.299508
- Max Citation Score: 599.50775
- Similarity Product: 523.4428200832009

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

Dataset: test