# Long-Tail Rebalancing for Non-Verbal Vocalization-Aware ASR: A Track 1 System for the NVVSpeech Challenge

arXiv: https://arxiv.org/abs/2609.23462 · HTML: https://mlsanity.com/paper/2609.23462/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 20 Sep 2026.

## Builds on (most cited first, 16 of 16)

- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Balanced Meta-Softmax for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2007.10740/) (2020)
- [Qwen3-ASR Technical Report](https://mlsanity.com/paper/2601.21337/) (2026)
- [Dynamic Curriculum Learning for Imbalanced Data Classification](https://mlsanity.com/paper/1901.06783/) (2019)
- [NVSpeech: An Integrated and Scalable Pipeline for Human-Like Speech Modeling with Paralinguistic Vocalizations](https://mlsanity.com/paper/2508.04195/) (2025)
- [NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech](https://mlsanity.com/paper/2507.13155/) (2025)
- [A Scalable Pipeline for Enabling Non-Verbal Speech Generation and Understanding](https://mlsanity.com/paper/2508.05385/) (2025)
- [SynParaSpeech: Automated Synthesis of Paralinguistic Datasets for Speech Generation and Understanding](https://mlsanity.com/paper/2509.14946/) (2025)
- [MNV-17: A High-Quality Performative Mandarin Dataset for Nonverbal Vocalization Recognition in Speech](https://mlsanity.com/paper/2509.18196/) (2025)
- [WavBench: Benchmarking Reasoning, Colloquialism, and Paralinguistics for End-to-End Spoken Dialogue Models](https://mlsanity.com/paper/2602.12135/) (2026)
- [Preference Optimization for Non-Verbal Vocalization Synthesis](https://mlsanity.com/paper/2608.24163/) (2026)
- [Preference Optimization with LALM Feedback for Continuous Autoregressive Non-Verbal Vocalization Generation](https://mlsanity.com/paper/2609.11260/) (2026)
- [WESR: Scaling and Evaluating Word-level Event-Speech Recognition](https://mlsanity.com/paper/2601.04508/) (2026)
- [Source-Adaptive Data Curation for Bilingual NVV-Aware ASR](https://mlsanity.com/paper/2609.09929/) (2026)
- [NVV-Locator: From Transcript Tags to Acoustic Boundaries for Fine-Grained Nonverbal Vocalization Grounding](https://mlsanity.com/paper/2609.09940/) (2026)

## Built on by (most cited first, 1 of 1)

- [Almost Human, Except When It Matters: VoxParity and the Decisions a Voice Should Change](https://mlsanity.com/paper/2609.35922/) (2026)

## Abstract (arXiv)

Non-verbal vocalizations (NVVs) carry important paralinguistic information but are often omitted by conventional automatic speech recognition (ASR) systems. The ISCSLP NVVSpeech Challenge requires joint transcription of lexical content and 16 NVV categories under limited and highly imbalanced supervision. We present a data-centric NVV-aware ASR pipeline based on cross-dataset label harmonization and a two-stage sampling schedule. We map heterogeneous source labels to the official taxonomy and exclude samples without a reliable mapping. Our schedule first uses square-root category sampling to moderate the long-tailed distribution and then applies uniform-category fine-tuning. On a fixed local validation split, square-root category sampling performs best among the tested single-stage settings. The final two-stage system obtains an official score of 63.86 and ranks fourth in Track 1.