An LLM-based revision method with phonetic-semantic context reduces named entity word error rate by up to 30% relative on a new 45-hour MIT classroom speech dataset.
Efficient memory manage- ment for large language model serving with pagedattention,
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WAND adapts AR-TTS models to constant complexity via windowed attention and distillation, cutting KV cache memory by up to 66.2% while preserving quality and achieving length-invariant latency.
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Improving Speech Recognition of Named Entities in Classroom Speech with LLM Revision and Phonetic-Semantic Context
An LLM-based revision method with phonetic-semantic context reduces named entity word error rate by up to 30% relative on a new 45-hour MIT classroom speech dataset.
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WAND: Windowed Attention and Knowledge Distillation for Efficient Autoregressive Text-to-Speech Models
WAND adapts AR-TTS models to constant complexity via windowed attention and distillation, cutting KV cache memory by up to 66.2% while preserving quality and achieving length-invariant latency.