PATENT CLAIM ANALYSIS

Application Number: 15905789
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-07
Patent Classification: ["704", "232000"]

Abstract:
A system and method are presented for neural network based feature extraction for acoustic model development. A neural network may be used to extract acoustic features from raw MFCCs or the spectrum, which are then used for training acoustic models for speech recognition systems. Feature extraction may be performed by optimizing a cost function used in linear discriminant analysis. General non-linear functions generated by the neural network are used for feature extraction. The transformation may be performed using a cost function from linear discriminant analysis methods which perform linear operations on the MFCCs and generate lower dimensional features for speech recognition. The extracted acoustic features may then be used for training acoustic models for speech recognition systems.

Claim (Index 1):
A method for training acoustic models in speech recognition systems, wherein the speech recognition system comprises a neural network, the method comprising the steps of: a. extracting acoustic features from a speech signal using the neural network; and b. processing the acoustic features into an acoustic model by the speech recognition system.

Metadata:
- Claim Count in Document: 17.0
- Percentile: 88.0
- Lexical Diversity: 2.0
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14985560', '14965637', '14500042', '15258799', '15258836']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2513627452602537
- 35 USC 102 Novelty (BERT): 0.4525476144362659
- Combined Prediction Score: 0.2714812321778549
- Mean Citation Score: 138.62086599999998
- Max Citation Score: 177.41527
- Similarity Product: 156.1011147734821

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

Dataset: test