Speech Processing Model Selection for Dynamic Acoustic Environments
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Solution Overview
Problem
Microphone arrays introduce time-varying spectral modifications to speech signals due to changes in speaker position and adaptive beamforming, affecting speech processing systems' robustness and accuracy.
Innovation Solution
A computer-implemented method that receives inputs on speaker location and microphone array orientation, generates time-varying spectrally-augmented signals by modeling acoustic variations, and trains speech processing systems to account for these changes, enabling more robust speech recognition in dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If microphone arrays are used to receive speech signals, then spatial selectivity and noise rejection are improved, but time-varying spectral modifications are introduced due to speaker position changes and adaptive beamforming
Solution Approach 1:
The system pre-trains multiple speech processing models with different acoustic characteristics (including various beamforming effects and speaker positions) before actual speech recognition. This preliminary preparation allows the system to quickly select an appropriate pre-trained model during runtime without needing to adapt in real-time, thus maintaining spectral accuracy while benefiting from microphone array noise rejection
Solution Approach 2:
The system dynamically selects among multiple pre-trained speech processing models based on runtime acoustic conditions (such as speaker position and beamforming configuration). This dynamic adaptation allows the system to maintain spectral accuracy by matching the current acoustic environment with the most appropriate pre-trained model, while still utilizing the noise rejection capabilities of the microphone array
2Measurement precision
If adaptive beamforming is used to steer towards speakers, then speech signal quality is improved, but time variations in the speech spectrum are introduced
Solution Approach 1:
Multiple speech processing models are pre-trained with different beamforming configurations and spectral characteristics. This preliminary action captures various beamforming effects in advance, allowing the system to select an appropriate model during runtime without introducing instability from real-time spectral variations
Solution Approach 2:
The system creates multiple copies of speech processing models, each trained with different beamforming configurations. These model copies represent different spectral conditions, allowing the system to select the most appropriate copy for the current acoustic environment, thereby maintaining spectral stability while benefiting from adaptive beamforming
3Adaptability or versatility
If speaker position varies in the beampattern, then spatial flexibility is improved, but speech signals are affected by time-varying filters
Solution Approach 1:
The system pre-trains speech processing models with speech signals captured from different spatial positions and beamforming configurations. This preliminary action creates a library of models that account for spatial variations, allowing the system to maintain speech signal consistency by selecting an appropriate pre-trained model for the current speaker position
Solution Approach 2:
The system changes the parameter of model selection based on spatial conditions. By adjusting which pre-trained model is used (rather than adapting the model itself in real-time), the system maintains speech signal consistency while accommodating spatial flexibility in speaker positioning
Data Source
AI summary
A method, computer program product, and computing system for receiving one or more inputs indicative of at least one of: a relative location of a speaker and a microphone array, and a relative orientation of the speaker and the microphone array. One or more reference signals may be received. A speech processing system may be trained using the one or more inputs and the one or more reference signals.