Contextual ASR Frontend for Joint Echo Cancellation and Voice Separation
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems face challenges in handling device echo, background noise, and competing speech interference, which are typically addressed in isolation, making it impractical to maintain multiple task-specific models and switch between them dynamically.
Innovation Solution
A contextual frontend processing model that integrates acoustic echo cancellation, speech enhancement, and voice separation into a single neural network architecture, utilizing reference signals, noise context, and speaker embeddings to enhance input speech features for robust ASR performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple separate models are maintained for acoustic echo cancellation, speech enhancement, and voice separation, then each specific task can be optimized, but the system complexity and difficulty of dynamic switching increase
Solution Approach 1:
The patent combines three separate models (acoustic echo cancellation, speech enhancement, and voice separation) into a single integrated contextual frontend processing model. This unified model receives multiple inputs simultaneously and produces enhanced speech features that incorporate all three processing functions, thereby reducing system complexity while maintaining task-specific optimization through specialized neural network components within the integrated architecture.
Solution Approach 2:
The integrated contextual frontend processing model is designed to perform multiple functions simultaneously - acoustic echo cancellation, speech enhancement, and voice separation - within a single universal framework. The model accepts diverse inputs (noisy speech, reference signal, noise context, speaker embedding) and produces enhanced output features that embody all three processing objectives, eliminating the need for separate specialized models.
2Measurement precision
If multiple task-specific models are maintained, then each function can be optimized independently, but the computational resources and model maintenance burden increase
Solution Approach 1:
By merging three separate processing models into one integrated contextual frontend processing model, the patent reduces the computational overhead of maintaining and switching between multiple models. The unified model processes all three functions (echo cancellation, speech enhancement, voice separation) in a single forward pass, improving model maintenance efficiency while preserving processing accuracy through specialized internal components.
3Reliability
If acoustic echo cancellation, speech enhancement, and voice separation are processed separately, then each processing step can be optimized, but the overall system robustness in dynamic environments decreases
Solution Approach 1:
The integrated contextual frontend processing model provides environmental adaptability by simultaneously handling multiple acoustic conditions (echo, noise, competing speech) within a single unified framework. The model receives contextual inputs including noise context and speaker embeddings, allowing it to adapt to dynamic environments while maintaining reliable processing through coordinated multi-functionality.
Data Source
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AI summary
A method (600) for automatic speech recognition using joint acoustic echo cancellation, speech enhancement, and voice separation includes receiving, at a contextual frontend processing model (200), input speech features (212) corresponding to a target utterance (12). The method also includes receiving, at the contextual frontend processing model, at least one of a reference audio signal (154), a contextual noise signal (213) including noise prior to the target utterance, or a speaker embedding (215) including voice characteristics of a target speaker (10) that spoke the target utterance. The method further includes processing, using the contextual frontend processing model, the input speech features and the at least one of the reference audio signal, the contextual noise signal, or the speaker embedding vector to generate enhanced speech features (250).