Neural Network Adaptive Beamforming for Speech Recognition
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Solution Overview
Problem
Existing automated speech recognition (ASR) systems face challenges in far-field conditions due to reverberation and noise, where fixed spatial and spectral filters fail to adapt to changing user positions, leading to decreased accuracy and increased computational complexity.
Innovation Solution
A neural network adaptive beamforming (NAB) system that dynamically generates spatial filters for each input frame using a filter prediction LSTM module, jointly trained with an acoustic model, to enhance speech recognition by adapting to the actual user position and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed spatial and spectral filters are used in traditional beamforming systems, then the system structure is simple and computational complexity is low, but the system cannot adapt to changing user positions and environments, leading to decreased speech recognition accuracy in far-field conditions
Solution Approach 1:
The patent implements dynamic filter adaptation by training a neural network to generate time-domain filters that change adaptively with each input frame based on the actual user position and environmental conditions, transforming the static fixed-filter system into a dynamic adaptive system that automatically adjusts to varying speech scenarios
Solution Approach 2:
The patent replaces the traditional iterative mechanical optimization process with a neural network-based learning system. Instead of using iterative algorithms to optimize beamforming parameters and acoustic model parameters separately, the system uses a neural network to directly generate adaptive filters, eliminating the need for complex iterative training procedures
2Reliability
If iterative parameter optimization is used to estimate beamforming parameters and acoustic model parameters separately, then the system can achieve reasonable performance, but the training process is complex and not easily combined with neural network-based acoustic models that use gradient learning algorithms
Solution Approach 1:
The patent merges the beamforming filter estimation and acoustic model training into a unified neural network framework. The neural network jointly learns to generate adaptive spatial and spectral filters while working with the acoustic model, combining previously separate iterative optimization processes into a single gradient-based learning system that is computationally more efficient and easier to implement
Solution Approach 2:
The patent substitutes the iterative parameter optimization mechanism with a neural network-based gradient learning approach. The system uses backpropagation and gradient descent to train the neural network to generate optimal filters directly, replacing the complex iterative alternating optimization process with a more straightforward and flexible learning algorithm
3Adaptability or versatility
If multiple spatial filters are learned to adapt to target signals from different spatial directions, then the system can handle various user positions, but the computational complexity increases significantly
Solution Approach 1:
The patent changes the parameter representation from a fixed set of spatial filters to dynamically generated time-domain filters produced by a neural network. Instead of maintaining multiple pre-defined spatial filters for different directions, the system uses the neural network to generate appropriate filters on-the-fly based on the current input frame, achieving full spatial coverage without the computational burden of managing multiple filter sets
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for neural network adaptive beamforming for multichannel speech recognition are disclosed. In one aspect, a method includes the actions of receiving a first channel of audio data corresponding to an utterance and a second channel of audio data corresponding to the utterance. The actions further include generating a first set of filter parameters for a first filter based on the first channel of audio data and the second channel of audio data and a second set of filter parameters for a second filter based on the first channel of audio data and the second channel of audio data. The actions further include generating a single combined channel of audio data. The actions further include inputting the audio data to a neural network. The actions further include providing a transcription for the utterance.