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Fireredasr2s: A state-of-the-art industrial-grade all-in-one automatic speech recognition system

3 Pith papers cite this work. Polarity classification is still indexing.

3 Pith papers citing it

citation-role summary

dataset 1

citation-polarity summary

years

2026 3

verdicts

UNVERDICTED 3

roles

dataset 1

polarities

use dataset 1

representative citing papers

Audio-Mind: An Auditable Agentic Framework for Audio Understanding

eess.AS · 2026-05-27 · unverdicted · novelty 4.0

Audio-Mind introduces a conditional, auditable agentic framework for audio understanding that preserves frontend judgment and acquires bounded external evidence only when needed, reporting 80.4% on MMAR and 82.8% on MSU-Bench.

Dolphin-CN-Dialect: Where Chinese Dialects Matter

cs.CL · 2026-05-09 · unverdicted · novelty 4.0

Dolphin-CN-Dialect is a compact ASR model that boosts Chinese dialect accuracy through balanced sampling of rare dialects and character-level tokenization while staying smaller than recent open-source competitors.

citing papers explorer

Showing 3 of 3 citing papers.

  • MSAVBench: Towards Comprehensive and Reliable Evaluation of Multi-Shot Audio-Video Generation cs.CV · 2026-05-19 · unverdicted · none · ref 72 · 2 links

    MSAVBench is the first comprehensive benchmark for multi-shot audio-video generation featuring four dimensions, challenging scenarios, and an adaptive hybrid evaluation framework that achieves 91.5% Spearman correlation with human judgments.

  • Audio-Mind: An Auditable Agentic Framework for Audio Understanding eess.AS · 2026-05-27 · unverdicted · none · ref 47

    Audio-Mind introduces a conditional, auditable agentic framework for audio understanding that preserves frontend judgment and acquires bounded external evidence only when needed, reporting 80.4% on MMAR and 82.8% on MSU-Bench.

  • Dolphin-CN-Dialect: Where Chinese Dialects Matter cs.CL · 2026-05-09 · unverdicted · none · ref 16

    Dolphin-CN-Dialect is a compact ASR model that boosts Chinese dialect accuracy through balanced sampling of rare dialects and character-level tokenization while staying smaller than recent open-source competitors.