Mixture Model Attention for Unified ASR
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Solution Overview
Problem
Current automatic speech recognition (ASR) systems require separate models for streaming and non-streaming speech recognition, leading to computational inefficiencies and increased memory requirements, as well as limitations in handling latency-sensitive applications and real-time transcription due to fixed context window sizes in transformer- and conformer-based models.
Innovation Solution
The implementation of a mixture model attention mechanism that allows the ASR model to switch between streaming and non-streaming modes by adjusting mixture weights, enabling flexible context window sizes and unified speech recognition capabilities using a single neural network architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate models are used for streaming and non-streaming speech recognition, then each model can be optimized for its specific purpose, but computational efficiency decreases and memory requirements increase
Solution Approach 1:
The patent combines streaming and non-streaming speech recognition capabilities into a single unified model architecture. The model uses a shared encoder-decoder structure with attention mechanisms that can dynamically adapt to different recognition modes, eliminating the need for separate specialized models while maintaining optimization for both streaming and non-streaming scenarios.
Solution Approach 2:
The unified model architecture is designed to perform multiple functions - it can operate in both streaming mode (for real-time transcription with low latency) and non-streaming mode (for higher accuracy offline recognition) using the same underlying structure. This multi-functionality is achieved through configurable attention mechanisms and context window management that adapt to different operational requirements.
2Device complexity
If fixed context window sizes are used in transformer- and conformer-based models, then model architecture is simplified, but flexibility in handling different applications is reduced
Solution Approach 1:
The patent implements dynamic context window sizing that allows the model to adapt its attention scope based on the specific application requirements. The context window size can be adjusted during inference to balance between latency (smaller windows for streaming) and accuracy (larger windows for non-streaming), providing flexibility without requiring multiple fixed-architecture models.
3Reliability
If larger context windows are used to improve recognition accuracy, then more future context is available, but computational costs and memory requirements increase
Solution Approach 1:
The model employs partial attention mechanisms that selectively attend to relevant portions of the context window rather than processing the entire sequence uniformly. This allows the model to access large context windows when needed for accuracy while reducing computational overhead by focusing attention only on the most relevant time steps and tokens, effectively implementing a form of computational sparsity.
Data Source
AI summary
A method for an automated speech recognition (ASR) model for unifying streaming and non-streaming speech recognition including receiving a sequence of acoustic frames. The method includes generating, using an audio encoder of an automatic speech recognition (ASR) model, a higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The method further includes generating, using a joint encoder of the ASR model, a probability distribution over possible speech recognition hypothesis at the corresponding time step based on the higher order feature representation generated by the audio encoder at the corresponding time step. The audio encoder comprises a neural network that applies mixture model (MiMo) attention to compute an attention probability distribution function (PDF) using a set of mixture components of softmaxes over a context window.