Introduces own-voice cancellation as a complement to target speaker extraction and benchmarks lightweight 2 ms latency models for far-field speech enhancement.
Distillation and Pruning for Scal- able Self-Supervised Representation-Based Speech Quality As- sessment,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
The paper announces the ISCSLP 2026 CoT-TTS Challenge with text- and audio-context tracks, large-scale bilingual datasets, and a Qwen3-based baseline requiring both reasoning output and speech generation.
citing papers explorer
-
Don't Listen to Me: A Lightweight, Low-Latency Model for Own-Voice Cancellation in Far-Field Speech Enhancement
Introduces own-voice cancellation as a complement to target speaker extraction and benchmarks lightweight 2 ms latency models for far-field speech enhancement.
-
ISCSLP 2026 CoT-TTS Challenge: Chain-of-Thought Reasoning for Context-Aware Text-to-Speech
The paper announces the ISCSLP 2026 CoT-TTS Challenge with text- and audio-context tracks, large-scale bilingual datasets, and a Qwen3-based baseline requiring both reasoning output and speech generation.