Speech Recognition Error Correction via Bidirectional RNN Linguistic Model
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Solution Overview
Problem
Current speech recognition methods suffer from recognition errors due to noise, leading to low accuracy in identifying the correct candidate answers.
Innovation Solution
A speech recognition method using a bidirectional recurrent neural network linguistic model to calculate the suitability of each word in a sentence, detect target words for correction, and replace them with candidate words based on their relationship, similarity to the phoneme sequence, and contextual context, improving the accuracy of the recognition result.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition is performed using conventional methods, then the process is simple and fast, but recognition accuracy is low due to noise and errors
Solution Approach 1:
The speech recognition system is divided into multiple independent components: acoustic model for phoneme recognition, linguistic model for word-level probability calculation, and separate correction modules for different types of errors. This segmentation allows each component to be optimized independently while improving overall accuracy through coordinated operation.
Solution Approach 2:
A linguistic model acts as an intermediary between the acoustic model and the final recognition output. The linguistic model calculates probability distributions for candidate words and phrases, mediating the transformation from acoustic signals to meaningful language by incorporating grammatical and contextual knowledge.
2Reliability
If a single linguistic model is used for speech recognition, then the system is simpler to implement, but it cannot effectively correct recognition errors in noisy environments
Solution Approach 1:
Multiple linguistic models with different functionalities are merged into a unified speech recognition system. The first linguistic model handles basic word probability calculation while the second linguistic model specializes in context-based error correction. Their combined operation provides both speed and accuracy.
Solution Approach 2:
The system dynamically adjusts the operation of different linguistic models based on the recognition context. When recognition errors are detected or likelihood is low, the system activates the second linguistic model for correction. This dynamic adaptation optimizes reliability without always incurring the full complexity cost.
3Measurement precision
If conventional speech recognition methods are used, then processing is faster, but the system cannot provide correct candidate answers due to recognition errors
Solution Approach 1:
The system performs preliminary error detection and correction by calculating likelihoods for multiple candidate words using the linguistic model before final output. This preliminary action identifies potential errors early in the processing pipeline, allowing corrections to be made before committing to a final recognition result.
Solution Approach 2:
The linguistic model provides feedback on the quality of recognition candidates by calculating probability distributions. When the feedback indicates low confidence or linguistic implausibility, the system triggers correction procedures, creating a feedback loop that continuously improves candidate answer accuracy.
Data Source
AI summary
A speech recognition method includes receiving a sentence generated through speech recognition, calculating a degree of suitability for each word in the sentence based on a relationship of each word with other words in the sentence, detecting a target word to be corrected among the words in the sentence based on the degree of suitability for each word, and replacing the target word with any one of candidate words corresponding to the target word.