Voxvietnam: A Large-scale Multi-genre Dataset For Vietnamese Speaker Recognition

Abstract

Recent research in speaker recognition aims to address vulnerabilities due to variations between enrolment and test utterances, particularly in the multi-genre phenomenon where the utterances are in different speech genres. Previous resources for Vietnamese speaker recognition are either limited in size or do not focus on genre diversity, leaving studies in multi-genre effects unexplored. This paper introduces VoxVietnam, the first multi-genre dataset for Vietnamese speaker recognition with over 187,000 utterances from 1,406 speakers and an automated pipeline to construct a dataset on a large scale from public sources. Our experiments show the challenges posed by the multi-genre phenomenon to models trained on a single-genre dataset, and demonstrate a significant increase in performance upon incorporating the VoxVietnam into the training process. Our experiments are conducted to study the challenges of the multi-genre phenomenon in speaker recognition and the performance gain when the

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyvu2024voxvietnam

Related papers

• Cn-celeb: Multi-genre Speaker Recognition (2020)15.10
• Voxceleb2: Deep Speaker Recognition (2018)23.96
• Voxceleb: A Large-scale Speaker Identification Dataset (2017)23.55
• Voxblink2: A 100K+ Speaker Recognition Corpus And The Open-set Speaker-identification Benchmark (2024)9.41
• Voxlingua107: A Dataset For Spoken Language Recognition (2020)14.15
• Voxsrc 2019: The First Voxceleb Speaker Recognition Challenge (2019)0.00
• Crowdspeech And Voxdiy: Benchmark Datasets For Crowdsourced Audio Transcription (2021)0.00
• CN-CELEB: A Challenging Chinese Speaker Recognition Dataset (2019)16.39