Context-Aware Speech Transcription Using Domain-Specific Corpus Correction
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Solution Overview
Problem
Conventional voice-to-text software fails to accurately differentiate between similar sounding phonetic words, leading to inaccurate transcriptions due to uniform transcription based on nearest phonetic similarity without considering context, which can be costly and time-consuming to correct.
Innovation Solution
A context-aware speech transcription method that involves obtaining and correcting speech corpora for specific domains, training sets are prepared to determine the optimal percentage for accurate transcription, using machine learning and semantic parsing to differentiate between correct and misused words based on context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional voice-to-text software uses uniform transcription based on nearest phonetic similarity, then transcription speed is maintained, but transcription accuracy deteriorates due to inability to differentiate between similar sounding phonetic words
Solution Approach 1:
The system performs preliminary actions by collecting domain-specific speech corpora and training data before actual transcription tasks. Speech corpora are gathered and annotated with context information in advance, and machine learning models are pre-trained on this data to learn domain-specific language patterns and context relationships, enabling accurate transcription when the models are deployed
Solution Approach 2:
The transcription system is segmented into multiple specialized components: phonetic recognition module, context analysis module, domain-specific language model, and correction module. Each component handles a specific aspect of the transcription process, allowing the system to achieve high accuracy through coordinated specialized functions rather than a single monolithic approach
2Measurement precision
If domain-specific speech corpora are obtained and corrected speech corpora are created, then transcription accuracy for target domain is improved, but data processing time and computational resources increase
Solution Approach 1:
Speech corpora are collected, annotated, and corrected in advance to create preprocessed training datasets specific to the target domain. This preliminary data preparation includes gathering domain-specific speech samples, annotating them with context information, and correcting errors before the models are trained, enabling faster and more accurate transcription deployment
Solution Approach 2:
The system adapts transcription parameters and model configurations based on the specific target domain. By changing parameters such as vocabulary lists, context window sizes, and model architecture to match domain characteristics, the system achieves high domain-specific accuracy without requiring excessive processing time for generic transcription tasks
3Measurement precision
If machine learning models are trained with optimal percentage of training sets, then transcription accuracy is improved, but training time and computational cost increase
Solution Approach 1:
The system uses an optimal percentage of training sets rather than training on all available data. By selecting and training on a carefully curated subset of training data that contains the most informative and diverse examples for the target domain, the system achieves high transcription accuracy while reducing training time and computational resources compared to using the complete dataset
Data Source
AI summary
The present inventive concept provided for context aware speech transcription. The method includes obtaining speech corpora for a target domain. A corrected speech corpora is created by editing misused words in the speech corpora with correct words for the target domain. The training sets are prepared based on the speech corpora and corrected speech corpora, and an optimal percentage of the training sets to use for accurate transcription of speech related to the target domain is determined.