Speech Recognition Using Multiple Accent Models
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Solution Overview
Problem
Current speech recognition systems face challenges in accurately recognizing spoken language due to variations in accents and dialects, often requiring users to specify their language version and struggling with deviations in pronunciation.
Innovation Solution
The implementation of speech recognition systems that utilize multiple recognition models for different accents and dialects of a language, which identify the language and select the appropriate models to produce recognition candidates, comparing scores to select the most accurate output, thereby accounting for variations in pronunciation without additional user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single recognition model is used for speech recognition, then the system is simple and easy to operate, but recognition accuracy decreases when dealing with different accents and dialects
Solution Approach 1:
The system employs multiple recognition models that can handle different versions of the same language (accents and dialects), allowing a single system to universally accommodate diverse speech patterns without requiring users to switch between different systems
Solution Approach 2:
The recognition system is segmented into multiple specialized models, each trained on specific language versions or accents. The system then selects and activates the appropriate segmented model based on the input audio characteristics, enabling high accuracy for each accent while maintaining overall system manageability
2Measurement precision
If users manually specify their language version, then the system can use the appropriate recognition model, but user input requirements increase and ease of operation decreases
Solution Approach 1:
The system performs automatic language version detection by analyzing acoustic characteristics of the input audio and autonomously selecting the appropriate recognition model without requiring users to manually specify their language version or provide additional configuration input
Solution Approach 2:
The system pre-configures multiple recognition models for different language versions and prepares language detection mechanisms in advance, enabling automatic model selection based on acoustic analysis before the actual recognition process begins
3Measurement precision
If multiple recognition models for different accents are implemented, then recognition accuracy improves, but the time required for processing increases
Solution Approach 1:
The system performs preliminary acoustic language version detection on the input audio to identify the dominant accent or dialect before initiating the recognition process, thereby determining which recognition model should be applied in advance and avoiding the need to process all models sequentially
Solution Approach 2:
Instead of processing all available recognition models exhaustively, the system applies partial action by selecting and processing only the most relevant model(s) based on acoustic detection results, or uses excessive action by preparing multiple models in advance while activating only the necessary ones, thus reducing processing time while maintaining high accuracy
Data Source
AI summary
Speech recognition systems may perform the following operations: receiving audio at a computing device; identifying a language associated with the audio; recognizing the audio using recognition models for different versions of the language to produce recognition candidates for the audio, where the recognition candidates are associated with corresponding information; comparing the information of the recognition candidates to identify agreement between at least two of the recognition models; selecting a recognition candidate based on information of the recognition candidate and agreement between the at least two of the recognition models; and outputting data corresponding to the selected recognition candidate as a recognized version of the audio.