Microphone Array Configuration-Aware Acoustic Modeling for Speech Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing speech processing systems face reduced accuracy when handling audio data from devices with microphone array configurations that do not match those for which the acoustic models are trained, leading to suboptimal phoneme association.
Innovation Solution
Incorporating microphone configuration data as an input vector to enhance acoustic models, allowing for the selection of appropriate models based on device-specific array configurations, and utilizing contextual data to improve phoneme association accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic models are trained for specific microphone array configurations, then speech recognition accuracy is improved for those configurations, but the system becomes less adaptable to devices with different microphone array configurations
Solution Approach 1:
The system changes the parameter of microphone configuration by encoding it as an input vector that modifies the acoustic model's processing. Instead of training separate models for each configuration, the configuration parameters are dynamically input to adjust the model's behavior, allowing the same model to adapt to different microphone array setups while maintaining high recognition accuracy
Solution Approach 2:
The acoustic model is designed to serve multiple functions by accepting microphone configuration data as input. A single universal model can process audio from various microphone array configurations by incorporating the configuration parameters into its processing, eliminating the need for separate specialized models for each device type
2Measurement precision
If microphone configuration data is incorporated as an input vector, then phoneme association accuracy is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The microphone configuration data is prepared and encoded as an input vector in advance, before the actual speech recognition processing. This preliminary encoding organizes the configuration information in a format ready for integration with audio features, reducing the computational burden during real-time processing by pre-structuring the data
Data Source
AI summary
Systems and methods for utilizing microphone array information for acoustic modeling are disclosed. Audio data may be received from a device having a microphone array configuration. Microphone configuration data may also be received that indicates the configuration of the microphone array. The microphone configuration data may be utilized as an input vector to an acoustic model, along with the audio data, to generate phoneme data. Additionally, the microphone configuration data may be utilized to train and/or generate acoustic models, select an acoustic model to perform speech recognition with, and/or to improve trigger sound detection.