Speech Recognition Correction Using Context and Pronunciation Cues
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Solution Overview
Problem
Existing speech recognition technologies face challenges in accurately detecting and correcting a wide variety of errors, particularly in multi-lingual contexts, leading to low correction accuracy, slow processing speeds, and propagation of recognition errors due to accent or pronunciation variations.
Innovation Solution
A speech recognition method that utilizes context information and pronunciation features to correct target text units, employs a bilingual recovery decoder with mask dual channel fusion to reduce noise, and uses a multi-encoder and language-aware decoder architecture to enhance accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing speech recognition technology is used to correct errors, then some errors can be detected, but a wide variety of speech recognition errors cannot be detected completely and correction accuracy remains low
Solution Approach 1:
The patent segments the speech recognition result into multiple text units and processes each unit independently through the correction model. This allows the system to handle different error types in different segments, improving both detection accuracy and coverage of various error patterns without overwhelming the correction system.
Solution Approach 2:
The patent introduces a correction model as an intermediary component between the initial speech recognition result and the final output. This correction model acts as a mediator that specifically targets and corrects various types of errors including pronunciation errors, accent errors, and linguistic errors, thereby improving error detection accuracy and coverage.
2Measurement precision
If the entire speech recognition result is re-processed to improve accuracy, then correction accuracy may improve, but processing speed decreases
Solution Approach 1:
The patent extracts and identifies target text units that are most likely to contain errors based on the initial recognition result, rather than re-processing the entire speech recognition output. This extraction approach allows the system to focus computational resources on correcting only the problematic segments, thereby maintaining high correction accuracy while significantly improving processing speed.
Solution Approach 2:
The patent applies partial action by processing only the identified target text units through the correction model rather than the entire speech recognition result. This partial processing approach achieves sufficient correction accuracy for the most critical error-prone segments while reducing overall processing time and computational load.
3Productivity
If speech recognition is performed without considering context and pronunciation features, then processing is faster, but error detection and correction capability is insufficient
Solution Approach 1:
The patent performs preliminary action by pre-processing the speech recognition result to identify target text units and extract relevant context and pronunciation features before applying the correction model. This preliminary preparation enables the correction model to focus on the most error-prone segments with pre-extracted features, improving error detection capability while maintaining processing efficiency.
Data Source
AI summary
A speech recognition method, an apparatus, an electronic device, and a computer-readable storage medium are provided. The method includes acquiring a first speech recognition result of a speech; acquiring context information and pronunciation feature information about a target text unit in the first speech recognition result; and acquiring a second speech recognition result of the speech based on the context information and the pronunciation feature information.