Token Merging for Multilingual Speech Recognition: A Systematic Study Across Model Scale and Fine-Tuning
Reducing the computational footprint of multilingual speech models through dynamic token consolidation.
Leading multilingual speech recognition models like Whisper transcribe diverse, low-resource languages without language-specific training but are computationally expensive to deploy. Token merging mitigates this inefficiency by dynamically combining redundant features, shortening...
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Token merging significantly lowers the computational overhead of large-scale speech models, making high-performance transcription accessible on resource-constrained devices. This breakthrough facilitates more equitable technology deployment by bridging the efficiency gap for complex, multilingual AI architectures.
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