Neural Mixture Component for Universal Acoustic Modeling
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Solution Overview
Problem
Acoustic models are highly domain-dependent and perform poorly when there is a mismatch between training and test conditions, such as different voices or microphones, and existing Mixture of Experts (MoE) systems suffer due to the gating network's unawareness of the expert models' state.
Innovation Solution
A universal acoustic modeling system that uses neural mixture components to generate weights based on information from domain expert models, such as hidden features or row convolution, to improve domain expert model output mixing, allowing for efficient combination of expert outputs without retraining the models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a Mixture of Experts model combines outputs of several domain specific models using a gating network that uses raw inputs directly, then multi-domain modeling capability is achieved, but system performance suffers due to the gating network's unawareness of the expert models' state
Solution Approach 1:
The gating network receives feedback from the expert models' hidden states and uses this information to dynamically adjust mixing weights. The expert-aware gating mechanism incorporates expert hidden states as inputs, allowing the gating network to adapt its weight assignments based on the current state of each expert model, thereby improving overall system performance while maintaining multi-domain capability
Solution Approach 2:
An expert-aware gating network acts as an intermediary between the raw inputs and the expert model outputs. This gating mechanism processes both raw inputs and expert hidden states to generate optimized mixing weights, serving as a mediator that coordinates information flow between inputs, experts, and final outputs while enhancing system performance
2Measurement precision
If domain specific acoustic models are trained for different domains (e.g., different voices or microphones), then domain-specific accuracy is improved, but the model performs poorly when there is a mismatch between training and test conditions
Solution Approach 1:
The Mixture of Experts framework creates a universal acoustic model that can handle multiple domains through a single unified system. The gating network dynamically selects and combines outputs from multiple domain-specific experts based on the input characteristics, allowing the system to adapt to unseen domains and mismatched conditions while maintaining high accuracy across different scenarios
3Device complexity
If the gating network uses raw inputs directly without considering expert model states, then system complexity is reduced, but the mixing of domain expert model outputs is inefficient
Solution Approach 1:
The gating network incorporates feedback from expert model hidden states to dynamically adjust mixing weights. By using expert-aware gating that processes both raw inputs and expert states, the system achieves efficient output mixing that adapts to the current state of each expert model, improving productivity without excessive complexity increase
Data Source
AI summary
According to some embodiments, a universal modeling system may include a plurality of domain expert models to each receive raw input data (e.g., a stream of audio frames containing speech utterances) and provide a domain expert output based on the raw input data. A neural mixture component may then generate a weight corresponding to each domain expert model based on information created by the plurality of domain expert models (e.g., hidden features and/or row convolution). The weights might be associated with, for example, constrained scalar numbers, unconstrained scaler numbers, vectors, matrices, etc. An output layer may provide a universal modeling system output (e.g., an automatic speech recognition result) based on each domain expert output after being multiplied by the corresponding weight for that domain expert model.