oboVox Far Field Speaker Recognition: A Novel Data Augmentation Approach with Pretrained Models
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929500828205056 |
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| author | Dip, Muhammad Sudipto Siam Hasan, Md Anik Bipro, Sapnil Sarker Raiyan, Md Abdur Motin, Mohammod Abdul |
| author_facet | Dip, Muhammad Sudipto Siam Hasan, Md Anik Bipro, Sapnil Sarker Raiyan, Md Abdur Motin, Mohammod Abdul |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_10240 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | oboVox Far Field Speaker Recognition: A Novel Data Augmentation Approach with Pretrained Models Dip, Muhammad Sudipto Siam Hasan, Md Anik Bipro, Sapnil Sarker Raiyan, Md Abdur Motin, Mohammod Abdul Audio and Speech Processing Sound 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 |
| title | oboVox Far Field Speaker Recognition: A Novel Data Augmentation Approach with Pretrained Models |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2409.10240 |