Far-Field Speech Calibration Using Spatial Acoustic Parameters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing far-field speech interaction systems face challenges in achieving optimal performance across diverse acoustic environments due to limitations in computing power and spatial acoustic parameter variability, leading to inconsistent speech recognition outcomes.
Innovation Solution
A method that calculates spatial acoustic parameters using soundwave network configuration signals to optimize front-end speech processing and update the speech recognition engine, integrating echo cancellation and beam forming filters based on these parameters to improve recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If wake-up module is deployed at device side to meet delay and stability requirements, then response speed is improved, but model size is limited due to embedded device computing power
Solution Approach 1:
The speech recognition system is divided into two segments: a lightweight wake-up model deployed on the embedded device for rapid response, and a larger main recognition model deployed on the cloud for comprehensive processing. This segmentation allows each component to be optimized for its specific function and deployment environment.
Solution Approach 2:
A parameter adaptation module acts as an intermediary between the wake-up model and main recognition model. It receives acoustic environment parameters from the wake-up model and adjusts them for use by the main recognition model, enabling efficient parameter transfer without direct coupling of the two models.
2Measurement precision
If adaptive speech recognition methods are adopted to improve speech interaction performance, then recognition accuracy in general scenarios is improved, but performance in noise scenarios and local playback scenarios deteriorates
Solution Approach 1:
The system dynamically changes acoustic environment parameters based on detected conditions. Different parameter sets are applied for different scenarios (general speech, noise, local playback), allowing the recognition model to adapt its behavior to match the current acoustic environment and maintain high accuracy across diverse conditions.
3Measurement precision
If transcription method is used to simulate far-field speech data sets, then far-field speech recognition performance is improved, but coverage of spatial acoustic parameters deteriorates due to variability in room characteristics
Solution Approach 1:
The system transitions from static simulated data to dynamic real-time measurement. Acoustic environment parameters are dynamically measured in the actual deployment environment and used to adapt the recognition model, ensuring high accuracy across diverse real-world scenarios without relying on limited simulated data coverage.
Solution Approach 2:
The system performs self-calibration by automatically measuring its own acoustic environment parameters during deployment. This self-service approach eliminates the need for extensive manual data collection and simulation across different room types, as the system adapts to its specific environment autonomously.
4Device complexity
If front-end speech processing parameters are fixed to simplify system design, then device complexity is reduced, but speech interaction performance in varying acoustic environments deteriorates
Solution Approach 1:
Acoustic environment parameters are measured and processed in advance during a calibration phase before actual speech recognition begins. This preliminary action allows the system to pre-adjust optimization parameters based on the specific acoustic environment, simplifying real-time processing while maintaining high performance.
Solution Approach 2:
The system implements a feedback loop where acoustic environment parameters are continuously monitored and used to adjust front-end speech processing parameters. This feedback mechanism enables automatic adaptation to varying acoustic conditions without requiring complex manual configuration or real-time computational optimization.
Data Source
AI summary
A method for improving far-field speech interaction performance, and a far-field speech interaction system are provided. The method comprises: a smart device, during a network configuration process by soundwave signals, acquires a soundwave transmitted by a network configuration auxiliary device, and calculates a spatial acoustic parameter where the smart device is located. Furthermore, numerical values of the spatial acoustic parameter are selected within a set margin range, front-end speech processing parameter is updated according to the spatial acoustic parameter, and a soundwave signal energy value after front-end speech processing is calculated so that a spatial acoustic parameter corresponding to the minimum energy value is selected as a final spatial acoustic parameter. The final spatial acoustic parameter is transmitted to a speech recognition engine which selects an acoustic model trained by using a data set closest to the final spatial acoustic parameter as a far-field speech recognition model.