# MoRoVoc: A Large Dataset for Geographical Variation Identification of the Spoken Romanian Language

arXiv: https://arxiv.org/abs/2509.16781 · HTML: https://mlsanity.com/paper/2509.16781/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Builds on (most cited first, 18 of 18)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Advances and Challenges in Meta-Learning: A Technical Review](https://mlsanity.com/paper/2307.04722/) (2023)
- [Speech Recognition Challenge in the Wild: Arabic MGB-3](https://mlsanity.com/paper/1709.07276/) (2017)
- [Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition](https://mlsanity.com/paper/1803.04567/) (2018)
- [Yet Another Model for Arabic Dialect Identification](https://mlsanity.com/paper/2310.13812/) (2023)
- [RoQLlama: A Lightweight Romanian Adapted Language Model](https://mlsanity.com/paper/2410.04269/) (2024)
- [Multi-Dialect Vietnamese: Task, Dataset, Baseline Models and Challenges](https://mlsanity.com/paper/2410.03458/) (2024)
- [HistNERo: Historical Named Entity Recognition for the Romanian Language](https://mlsanity.com/paper/2405.00155/) (2024)
- [RoDia: A New Dataset for Romanian Dialect Identification from Speech](https://mlsanity.com/paper/2309.03378/) (2023)
- [Speech Analysis of Language Varieties in Italy](https://mlsanity.com/paper/2406.15862/) (2024)
- [RoLargeSum: A Large Dialect-Aware Romanian News Dataset for Summary, Headline, and Keyword Generation](https://mlsanity.com/paper/2412.11317/) (2024)
- [Learning Intonation Pattern Embeddings for Arabic Dialect Identification](https://mlsanity.com/paper/2008.00667/) (2020)

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

- [RoIt-XMASA: Multi-Domain Multilingual Sentiment Analysis Dataset for Romanian and Italian](https://mlsanity.com/paper/2604.17134/) (2026)

## Abstract (arXiv)

This paper introduces MoRoVoc, the largest dataset for analyzing the regional variation of spoken Romanian. It has more than 93 hours of audio and 88,192 audio samples, balanced between the Romanian language spoken in Romania and the Republic of Moldova. We further propose a multi-target adversarial training framework for speech models that incorporates demographic attributes (i.e., age and gender of the speakers) as adversarial targets, making models discriminative for primary tasks while remaining invariant to secondary attributes. The adversarial coefficients are dynamically adjusted via meta-learning to optimize performance. Our approach yields notable gains: Wav2Vec2-Base achieves 78.21% accuracy for the variation identification of spoken Romanian using gender as an adversarial target, while Wav2Vec2-Large reaches 93.08% accuracy for gender classification when employing both dialect and age as adversarial objectives.