PATENT CLAIM ANALYSIS

Application Number: 15917082
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-09
Patent Classification: ["704", "232000"]

Abstract:
Non-limiting examples of the present disclosure describe advancements in acoustic-to-word modeling that improve accuracy in speech recognition processing through the replacement of out-of-vocabulary (OOV) tokens. During the decoding of speech signals, better accuracy in speech recognition processing is achieved through training and implementation of multiple different solutions that present enhanced speech recognition models. In one example, a hybrid neural network model for speech recognition processing combines a word-based neural network model as a primary model and a character-based neural network model as an auxiliary model. The primary word-based model emits a word sequence, and an output of character-based auxiliary model is consulted at a segment where the word-based model emits an OOV token. In another example, a mixed unit speech recognition model is developed and trained to generate a mixed word and character sequence during decoding of a speech signal without requiring generation of OOV tokens.

Claim (Index 15):
A system comprising:\n at least one processor; and a memory, operatively connected with the at least one processor, storing computer-executable instructions that, when executed by the at least one processor, causes the at least one processor to execute a method that comprises:\n receiving a speech signal; \n decoding the speech signal using a mixed unit speech recognition model that is trained based on word and character sequences; \n generating, for the speech signal, a mixed word and character sequence based on an evaluation of the speech signal by the mixed unit speech recognition model; \n decomposing the mixed word and character sequence; and \n outputting a speech recognition result for the speech signal that comprises the decomposed mixed word and character sequence.

Metadata:
- Claim Count in Document: 33.0
- Percentile: 90.0
- Lexical Diversity: 1.98734
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15843055', '10109251', '13906654', '13813463', '15878113']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2641522999781503
- 35 USC 102 Novelty (BERT): 0.4947607963456326
- Combined Prediction Score: 0.2872131496148985
- Mean Citation Score: 230.97057
- Max Citation Score: 252.57828
- Similarity Product: 189.2274286007166

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