mlsanityData to 19 Oct 2026

oboVox Far Field Speaker Recognition: A Novel Data Augmentation Approach with Pretrained Models

arXiv 2409.10240 ↗First posted 16 Sep 2024eess.AS cs.SDcited by 0

On the map: Speech and audio language models → Speaker recognition and diarization

Abstract (arXiv)

In this study, we address the challenge of speaker recognition using a novel data augmentation technique of adding noise to enrollment files. This technique efficiently aligns the sources of test and enrollment files, enhancing comparability. Various pre-trained models were employed, with the resnet model achieving the highest DCF of 0.84 and an EER of 13.44. The augmentation technique notably improved these results to 0.75 DCF and 12.79 EER for the resnet model. Comparative analysis revealed the superiority of resnet over models such as ECPA, Mel-spectrogram, Payonnet, and Titanet large. Results, along with different augmentation schemes, contribute to the success of RoboVox far-field speaker recognition in this paper

Key facts. No citing papers in the map yet (data through 19 Oct 2026). Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models. Builds on 8 of its 12 references that are in the map. First posted on arXiv 16 Sep 2024.

Builds on

8 of 12 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context8 Oct

Other references · 7

  1. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,636
  2. 2018VoxCeleb2: Deep Speaker Recognitioncited by 1,166
  3. 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 980
  4. 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 532
  5. 2019pyannote.audio: neural building blocks for speaker diarizationcited by 217
  6. 2018Speaker Recognition from Raw Waveform with SincNetcited by 205
  7. 2023Cross-modal Audio-visual Co-learning for Text-independent Speaker Verificationcited by 7

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  1. 2025Adaptive Data Augmentation with NaturalSpeech3 for Far-field Speaker Verification
  2. 2024A Comprehensive Investigation on Speaker Augmentation for Speaker Recognition
  3. 2024Team HYU ASML ROBOVOX SP Cup 2024 System Description
  4. 2022The SpeakIn Speaker Verification System for Far-Field Speaker Verification Challenge 2022
  5. 2024Voice Conversion Augmentation for Speaker Recognition on Defective Datasets
  6. 2023Leveraging In-the-Wild Data for Effective Self-Supervised Pretraining in Speaker Recognition
  7. 2024VoxBlink2: A 100K+ Speaker Recognition Corpus and the Open-Set Speaker-Identification Benchmark
  8. 2021The HCCL Speaker Verification System for Far-Field Speaker Verification Challenge