Dual-Mode Speech Recognition Model for Latency-Accuracy Balance
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Solution Overview
Problem
Existing speech recognition technologies face challenges in balancing low-latency streaming recognition accuracy and fully contextual recognition accuracy, requiring separate models for different scenarios, which increases computational costs and complexity.
Innovation Solution
A dual-mode machine-learned speech recognition model that can switch between streaming and contextual recognition modes, using a contextual recognition mode to enhance the accuracy and latency of the streaming mode through distillation training, eliminating the need for separate models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate models are used for streaming and contextual recognition, then each mode can be optimized for its specific task, but device complexity and computational costs increase
Solution Approach 1:
The patent combines streaming and contextual recognition capabilities into a single unified model architecture. The model uses shared encoder and decoder components that can operate in different modes through configuration rather than requiring separate model instances, thereby reducing device complexity while maintaining optimization for both recognition tasks.
Solution Approach 2:
The unified model is designed with multi-functionality to handle both streaming and contextual recognition tasks. By implementing mode-switching mechanisms and shared computational components that can adapt to different operational requirements, the model achieves universal applicability across multiple speech recognition scenarios without requiring separate specialized models.
2Speed
If streaming recognition is used for low-latency tasks, then response speed is improved, but prediction accuracy deteriorates
Solution Approach 1:
The model implements dynamic operation modes that can adapt between streaming and contextual recognition based on task requirements. The streaming mode provides fast response for time-critical applications, while the contextual mode enhances accuracy when additional processing time is available, allowing the system to dynamically adjust its behavior to balance speed and accuracy trade-offs.
Solution Approach 2:
The system changes operational parameters such as processing configuration and context utilization based on the recognition task. By adjusting these parameters, the model can optimize for either speed (streaming mode with incremental processing) or accuracy (contextual mode with full-sequence processing), resolving the contradiction between response speed and prediction accuracy.
3Measurement precision
If contextual recognition is used for high-accuracy tasks, then prediction accuracy is improved, but latency increases
Solution Approach 1:
The model employs dynamic mode selection to adjust processing behavior based on task requirements. For time-sensitive applications, the streaming mode provides rapid responses with acceptable accuracy, while for accuracy-critical tasks, the contextual mode is activated to maximize prediction precision, thereby dynamically managing the trade-off between accuracy and latency.
Solution Approach 2:
The system modifies processing parameters such as context window size and decoding strategy based on the specific recognition task. By changing these parameters, the model can reduce latency when full contextual analysis is not critical, or enhance accuracy when time permits, effectively managing the latency-accuracy trade-off through parameter optimization.
4Reliability
If multiple separate models are maintained for different recognition modes, then task-specific optimization is achieved, but computational costs and resource usage increase
Solution Approach 1:
The patent merges multiple recognition capabilities into a single computational model with shared parameters and architecture. This consolidation eliminates the need to load and execute multiple separate models, reducing memory usage and computational overhead while maintaining specialized performance for different tasks through mode-specific configuration and training.
Data Source
AI summary
Systems and methods of the present disclosure are directed to a computing system, including one or more processors and a machine-learned multi-mode speech recognition model configured to operate in a streaming recognition mode or a contextual recognition mode. The computing system can perform operations including obtaining speech data and a ground truth label and processing the speech data using the contextual recognition mode to obtain contextual prediction data. The operations can include evaluating a difference between the contextual prediction data and the ground truth label and processing the speech data using the streaming recognition mode to obtain streaming prediction data. The operations can include evaluating a difference between the streaming prediction data and the ground truth label and the contextual and streaming prediction data. The operations can include adjusting parameters of the speech recognition model.