Streaming Array-Agnostic Neural Frontend for Robust ASR
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems struggle with robustness in the presence of background interference such as device echo, background noise, and competing speech, particularly in low signal-to-noise ratio conditions, and maintaining multiple task-specific models is impractical.
Innovation Solution
A multichannel neural frontend speech enhancement model that includes pre-processing blocks, self-attention blocks, masking layers, and a phase extraction layer to enhance speech features by processing multichannel noisy input signals, effectively removing background interference using a contextual enhancement neural network (CENN) that is agnostic to the number of microphones in the array.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a joint ASR model is trained to handle reverberation, background noise, and competing speech, then robustness of the ASR system is improved, but the complexity of the model and training process increases significantly
Solution Approach 1:
The system segments the speech enhancement task into multiple specialized modules: a multichannel noise estimator for background noise modeling, a speech separator for isolating target speech from competing speakers, and a speech recognizer. This modular segmentation allows each component to be optimized independently while maintaining overall system robustness without requiring a single monolithic complex model
Solution Approach 2:
The noise estimator and speech separator are designed as universal preprocessing components that can be applied across different ASR scenarios and conditions. The noise estimator adapts to various background noise types (reverberation, traffic, crowd noise) without retraining, and the speech separator handles both single and multiple competing speakers, providing multi-functional capability that reduces the need for multiple task-specific models
2Reliability
If multiple task-specific models are maintained for different noise conditions, then performance in specific conditions is improved, but the practicality and ease of operation deteriorates due to model management complexity
Solution Approach 1:
The system employs universal preprocessing components (noise estimator and speech separator) that can handle multiple noise conditions and speech scenarios without requiring separate models. The noise estimator adapts to different background conditions (reverberation, traffic, crowd) through online adaptation, and the speech separator works for both single and multiple competing speakers, providing condition-agnostic performance that simplifies deployment
Solution Approach 2:
The system performs preliminary speech enhancement and separation before recognition, preparing a cleaned and separated speech signal that can be processed by a single ASR model. This preliminary action of noise estimation and speech separation enables one model to handle multiple conditions effectively, eliminating the need to switch between multiple task-specific models during operation
Data Source
AI summary
A multichannel neural frontend speech enhancement model includes a first pre-processor block, a second pre-processor block, a stack of self-attention blocks, a first masking layer, a second masking layer, and a phrase extraction layer. The first pre-processor receives short-time Fourier transform (STFT) coefficients for a single channel cleaned input signal and generates a maximum value of an embedding dimension of the cleaned input signal. The second pre-processor receives STFT coefficients for a single channel noisy input signal and generates a maximum value of an embedding dimension of the noisy input signal. The stack of self-attention blocks receives a stacked input of the embedding dimensions of the cleaned input signal and the noisy input signal and generates an un-masked output. The phrase extraction layer receives the un-masked output, a masked cleaned input signal, and a masked noisy input signal, and generate enhanced input speech features.