M o R o V oc: A Large Dataset for Geographical Variation Identification of the Spoken R omanian Language (2025)

Abstract: 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.

## introduction dialect identification poses a fundamental challenge in speech processing, and low-resource languages face persistent difficulties due to the scarcity of annotated corpora. despite its millions native speakers, romanian remains underrepresented in speech technology research. we introduce morovoc, the largest corpus for romanian spoken dialect identification to date, containing 93+ hours of annotated speech with @xcite audio samples balanced between standard romanian and moldavian dialects. the dataset includes comprehensive demographic metadata and is derived from high-quality parliamentary recordings. our methodological contribution introduces a novel multi-target adversarial training framework for fine-tuning speech models through gradient reversal @xcite . this approach * corresponding author. our experiments with wav2vec2 @xcite demonstrate that our multi-target adversarial training significantly improves performance across all classification tasks. for spoken dialect identification, wav2vec2-base achieves 78.21% accuracy (a 5.20% improvement over baseline) when using gender as adversarial target, while wav2vec2-large reaches 77.53% accuracy when both gender and age serve as adversarial targets. the main contributions of our work can be summarized as follows: ## methodology we introduce a novel fine-tuning strategy for pretrained wav2vec2 models incorporating multiple adversarial targets. our approach ensures invariance to demographic attributes, including dialect, gender, and age. we consider three configurations, each with a different main task and the remaining attributes as adversarial objectives: 1) spoken dialect identification as the main task with gender and age as adversarial goals, 2) gender classifica-tion as the primary objective with dialect and age as adversarial tasks, and 3) age classification with dialect and gender as adversarial tasks. ## results ## conclusion this paper introduced morovoc, the largest corpus of romanian spoken dialect identification to date, comprising over 93 hours of speech and 88,192 audio samples balanced between standard romanian and moldavian dialects, with comprehensive gender and age annotations. we also proposed a novel multi-target adversarial training framework for fine-tuning speech models that incorporates demographic attributes as adversarial targets with coefficients dynamically adjusted through metalearning, yielding significant performance improvements across all classification tasks: wav2vec2-base achieved 78.21% accuracy for dialect identification (a 5.20% improvement over baseline) when using gender adversarially. at the same time, wav2vec2-large reached 93.08% accuracy for gen-der classification when using both dialect and age adversarially. binary dialect classification (standard romanian vs. moldavian) also simplifies romania's dialectal landscape, which traditionally includes five major regional variants as seen in the rodia dataset @xcite . future work should diversify speech registers, improve demographic balance, and evaluate performance in various acoustic environments and model architectures. additionally, exploring the application of our multi-target adversarial training framework to other speech recognition tasks beyond dialect identification could further validate its effectiveness.