arXiv:2606.04678v1 Announce Type: new Abstract: End-to-end ASR systems typically use fixed-depth acoustic encoders at inference, making it difficult to trade additional test-time computation for improved recognition without training a larger model. A natural approach is to reuse a shared Transformer block recurrently, but we find that naive looping does not fully exploit additional recurrent compute. We introduce LARM, a depth-conditioned looped Transformer that turns recurrent encoder depth into a controllable test-time compute axis. LARM combines sparse CTC checkpoints, supervision-clock emb
The continuous drive for more efficient and adaptable AI models, particularly in resource-intensive areas like ASR, pushes for innovations that optimize compute usage.
This breakthrough offers a method to dynamically adjust compute power for ASR systems post-training, directly impacting the operational costs and performance flexibility of AI deployments.
ASR systems can now better trade between computational resources and recognition accuracy at test-time without requiring re-training, enabling more efficient deployment in diverse environments.
- · AI service providers
- · Cloud computing platforms
- · Hardware manufacturers (efficient architectures)
- · Autonomous systems developers
- · Fixed-model deployment strategies
- · High-cost, inefficient ASR solutions
More cost-effective and adaptable voice-activated systems become prevalent across various applications.
Reduced operational expenses for AI model inference could accelerate the adoption of complex AI in edge devices and resource-constrained environments.
The methodology could inspire similar test-time compute scaling in other large AI models, leading to a broader optimization trend in AI infrastructure.
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Read at arXiv cs.LG