PATENT CLAIM ANALYSIS

Application Number: 15860706
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2018-11
Patent Classification: ["704", "232000"]

Abstract:
Embodiments of the present disclosure provide a method and a device for recognizing a speech based on a Chinese-English mixed dictionary. The method includes acquiring a Chinese-English mixed dictionary marked by an international phonetic alphabet, in which, the Chinese-English mixed dictionary includes a Chinese dictionary and an English dictionary revised by Chinglish; by taking the Chinese-English mixed dictionary as a training dictionary, taking a one-layer Convolutional Neural Network and a five-layer Long Short-Term Memory as a model, taking syllables or words as a target and taking a connectionist temporal classifier as a training criterion, training the model to obtain a trained CTC acoustic model; and performing a speech recognition on a Chinese-English mixed language based on the trained CTC acoustic model.

Claim (Index 7):
The method according to  claim 1 , wherein, by taking the Chinese-English mixed dictionary as the training dictionary, taking the one-layer CNN and the five-layer LSTM as the model, taking syllables or words as the target and taking the CTC as the training criterion, training the model to obtain the trained CTC acoustic model comprises:\n extracting feature points in a Chinglish sentence using a filter bank FBANK as inputs of the model, and training the model by taking the one-layer CNN and the five-layer LSTM as the model, taking a matching file corresponding to the Chinglish sentence as the target and taking a Cross Entropy CE as the training criterion, to obtain an initial model; and training the initial model by taking the Chinese-English mixed dictionary as the training dictionary, taking the initial model as the model, taking the syllables or words as the target and taking the CTC as the training criterion, to obtain the trained CTC acoustic model.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 86.0
- Lexical Diversity: 2.03125
- Patent Class: 704.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['15859796', '10403747', '12698963', '10638750', '12248924']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2553303245561689
- 35 USC 102 Novelty (BERT): 0.6766105960163878
- Combined Prediction Score: 0.2974583517021908
- Mean Citation Score: 336.325832
- Max Citation Score: 679.56995
- Similarity Product: 648.8764020089685

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