MoE Conformer Routing for Low-Latency Multilingual ASR
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Solution Overview
Problem
Deep learning-based automatic speech recognition (ASR) models are resource-intensive due to over-parameterization, making them unsuitable for devices with limited computing resources and memory, and multilingual ASR models increase computational and storage costs by separately executing language identification models.
Innovation Solution
A multilingual ASR model with a cascaded encoder structure and decoders that include multi-head attention layers with mixture-of-experts (MoE) layers dynamically routing outputs to the highest-weighted feed-forward expert networks, reducing resource requirements while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are over-parameterized to improve speech recognition accuracy, then recognition accuracy is improved, but computational resource requirements and memory increase
Solution Approach 1:
The patent segments the monolithic deep neural network into multiple specialized expert networks (e.g., language-specific experts, domain-specific experts). Each expert network is smaller and more focused, processing specific types of inputs. The gating mechanism selectively activates only the relevant experts for each input, reducing the computational resources required while maintaining or improving recognition accuracy through specialized processing.
Solution Approach 2:
The patent introduces dynamic routing through a gating mechanism that adaptively selects which expert networks to activate based on the specific input characteristics. This dynamic activation allows the system to adjust computational resource usage in real-time, activating only the necessary experts for each speech input rather than constantly running all parameters, thus reducing energy consumption while preserving accuracy when needed.
2Measurement precision
If deep neural networks include more parameters to improve accuracy, then speech recognition accuracy is improved, but training time and convergence steps increase
Solution Approach 1:
By dividing the large parameterized network into smaller expert networks, each expert can be trained more efficiently on specific subsets of data relevant to its specialization. This segmentation allows for faster convergence of individual experts compared to training one massive network on all data, reducing overall training time while achieving comparable or better accuracy through the collective expertise of multiple specialized networks.
Solution Approach 2:
The gating mechanism enables partial action by activating only the necessary subset of expert networks for each input rather than engaging all parameters. During training, this means only relevant experts need to be updated for each training example, significantly reducing the computational burden per training step and accelerating convergence while still achieving high accuracy through the cumulative effect of specialized processing.
3Measurement precision
If multilingual ASR models separately execute language identification models, then language identification accuracy is improved, but computational cost and storage requirements increase
Solution Approach 1:
The patent merges the language identification function with the speech recognition experts by training language-specific expert networks that inherently perform both language identification and speech recognition. The gating mechanism determines which language expert to activate, effectively performing language identification without requiring a separate model. This integration reduces computational cost and storage requirements by eliminating redundant components while maintaining accurate language identification through the specialized language experts.
Solution Approach 2:
Each expert network is designed to be multi-functional, serving both as a language-specific processor and as a speech recognition engine. The experts can handle multiple tasks (language identification, speech recognition, domain-specific processing) within a single network, reducing the need for separate specialized models and thereby lowering overall computational cost and device complexity while maintaining high accuracy through specialized processing.
Data Source
AI summary
A method of a multilingual ASR model includes receiving a sequence of acoustic frames characterizing an utterance of speech. At a plurality of output steps, the method further includes generating a first higher order feature representation for an acoustic frame by a first encoder that includes a first plurality of multi-head attention layers; generating a second higher order feature representation for a corresponding first higher order feature representation by a second encoder that includes a second plurality of multi-head attention layers; and generating, by a first decoder, a first probability distribution over possible speech recognition hypotheses based on the second higher order feature representation and a sequence of N previous non-blank symbols. A gating layer of each respective MoE layer configured to dynamically route an output from a previous multi-head attention layer at each of the plurality of output steps to a respective pair of feed-forward expert networks.