newsHugging FaceTrust 88 · LabPublished 5d agoLive · 4d ago
Measuring benchmark optimization in speech recognition
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 63%Benchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial Results →
- PossiblePossibly related (embedding) · 57%NAVER LABS Europe Submission to the Instruction-following 2026 Short Track →
- PossiblePossibly related (embedding) · 56%Towards a Phonology-Informed Evaluation of Multilingual TTS →
- PossiblePossibly related (embedding) · 55%erogol/BlaGPT →
- PossiblePossibly related (embedding) · 55%Tone-Conditioned Curriculum Learning for Low-Resource Bantu Speech Recognition →
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paperBenchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial ResultspaperNAVER LABS Europe Submission to the Instruction-following 2026 Short TrackpaperTowards a Phonology-Informed Evaluation of Multilingual TTSrepoerogol/BlaGPTpaperTone-Conditioned Curriculum Learning for Low-Resource Bantu Speech Recognition
Related across the graph
paperBenchmarking Human and Automatic Speech Recognition of Diverse Speech: Initial ResultspaperTowards a Phonology-Informed Evaluation of Multilingual TTSrepoerogol/BlaGPTpaperNAVER LABS Europe Submission to the Instruction-following 2026 Short TrackpaperTone-Conditioned Curriculum Learning for Low-Resource Bantu Speech Recognition