Dual-Mode Speech Recognition Model for Latency-Accuracy Tradeoffs
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Solution Overview
Problem
Existing speech recognition technologies face challenges in achieving both low-latency streaming recognition and high-accuracy contextual recognition, with separate models required for each mode, leading to increased computational costs and complexity.
Innovation Solution
A dual-mode machine-learned speech recognition model that can switch between streaming and contextual recognition modes, utilizing a dual-mode layer architecture and a combined loss function to distill contextual accuracy into streaming mode, reducing 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 independently, but device complexity and computational costs increase
Solution Approach 1:
The patent combines streaming and contextual recognition capabilities into a single unified model. The model includes both a streaming recognition path that processes speech in real-time and a contextual recognition path that processes complete speech segments, allowing both functions to coexist in one model structure rather than requiring separate models for each mode.
Solution Approach 2:
The unified model is designed to perform multiple functions: it can operate in streaming mode for low-latency applications and in contextual mode for high-accuracy applications. The model structure incorporates components that enable both recognition approaches, making it a universal solution that adapts to different operational requirements without needing separate specialized models.
2Loss of time
If streaming recognition is used, then low-latency performance is achieved, but prediction accuracy deteriorates
Solution Approach 1:
The model segments the recognition process into two distinct paths: a streaming path that processes speech incrementally for low-latency applications, and a contextual path that processes complete speech segments for high-accuracy applications. This segmentation allows the system to choose the appropriate path based on latency requirements while maintaining both capabilities within the unified model.
Solution Approach 2:
The model dynamically switches between streaming and contextual recognition modes depending on the application requirements. The streaming mode is activated when low-latency is critical, while the contextual mode is activated when maximum accuracy is needed. This dynamic adaptability allows the single model to optimize performance for different operational scenarios.
3Measurement precision
If contextual recognition is used, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The model segments the recognition process into two distinct paths: a streaming path that processes speech incrementally for low-latency applications, and a contextual path that processes complete speech segments for high-accuracy applications. This segmentation allows the system to choose the appropriate path based on latency requirements while maintaining both capabilities within the unified model.
Solution Approach 2:
The model dynamically switches between streaming and contextual recognition modes depending on the application requirements. The streaming mode is activated when low-latency is critical, while the contextual mode is activated when maximum accuracy is needed. This dynamic adaptability allows the single model to optimize performance for different operational scenarios.
4Reliability
If multiple separate models are maintained, then mode-specific optimization is achieved, but computational costs and resource utilization increase
Solution Approach 1:
The patent combines streaming and contextual recognition capabilities into a single unified model. The model includes both a streaming recognition path that processes speech in real-time and a contextual recognition path that processes complete speech segments, allowing both functions to coexist in one model structure rather than requiring separate models for each mode.
Solution Approach 2:
The unified model is designed to perform multiple functions: it can operate in streaming mode for low-latency applications and in contextual mode for high-accuracy applications. The model structure incorporates components that enable both recognition approaches, making it a universal solution that adapts to different operational requirements without needing separate specialized models.
Data Source
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Figure 1C
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.