Speech Recognition Model Selection by Input Characteristics
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Solution Overview
Problem
Existing speech recognition systems face difficulties in decoding non-native, noisy, and children's speech, as they are typically trained on adult native speaker data, leading to performance issues with diverse speech types.
Innovation Solution
An automatic speech recognition system that selects and adapts multiple speech models based on input characteristics, using a server with a sound feature extractor, context profiler, and speech classifier to classify speech data and identify the best phoneme and sound map for decoding, continuously updating models with user-specific data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a speech recognition system is trained on adult native speaker data, then it achieves high accuracy for standard speech, but it fails to decode non-native, noisy, and children's speech effectively
Solution Approach 1:
The system segments the speech recognition task by dividing speech inputs into different categories (adult native speakers, children, non-native speakers, noisy environments) and assigns specialized speech models to each category. This segmentation allows the system to optimize accuracy for each specific speech type rather than using a single general-purpose model.
Solution Approach 2:
The system changes parameters by selecting different speech models based on detected speech characteristics. The server analyzes input speech and dynamically adjusts which model is used, effectively changing the recognition parameters to match the specific speech type being processed, thereby maintaining high accuracy across diverse inputs.
2Adaptability or versatility
If multiple specialized speech models are maintained for different speech types, then the system can handle diverse speech inputs, but the system complexity increases
Solution Approach 1:
The server acts as an intermediary between the speech input and the multiple speech models. It receives speech inputs, analyzes characteristics, selects the appropriate model, and routes the input to the correct model. This intermediary layer manages the complexity by centralizing the decision-making logic and abstracting the multiple models behind a unified interface.
Solution Approach 2:
The server is designed with multi-functionality, serving as both the analysis engine that detects speech characteristics and the routing mechanism that selects appropriate models. This universal component handles multiple tasks (analysis, selection, routing) within a single system element, reducing overall system complexity despite maintaining multiple specialized models.
3Measurement precision
If speech data is continuously collected and used to update models, then the system improves accuracy over time, but more data processing and storage resources are required
Solution Approach 1:
The system performs preliminary actions by continuously collecting speech data and updating models in the background before new speech inputs arrive. This proactive model updating ensures that the system is already optimized for recently encountered speech patterns when new inputs come in, improving accuracy without requiring intensive real-time processing.
Solution Approach 2:
The system implements feedback by using decoded speech outputs to continuously refine and update the speech models. The server analyzes performance on recent inputs and adjusts models accordingly, creating a closed-loop system that automatically improves accuracy over time based on actual usage patterns and performance data.
Data Source
AI summary
A method and system method for automatic speech recognition using selection of speech models based on input characteristics is disclosed herein. The method includes obtaining speech data from a speaker utilizing a microphone or an audio upload. The system and method select the best speech recognition model to automatically decode the input speech and continuously update models by updating/creating models in a database based on users speech abilities.