# Comparison of Modern Deep Learning Models for Speaker Verification
## 6 Conclusions  
In summary, our study provides a comprehensive comparison of modern deep learning models for speaker verification based on speaker embeddings on a custom-created dataset. The findings highlight the robust capabilities of speaker embeddings in this domain and provide valuable insights for further research in this field. Out of all the tested speaker verification models, the TitaNet and Ecapa models emerged as superior, with the lowest Equal Error Rates of 1.91% and 1.71%, respectively. While Pyannote trailed with a higher EER of 3.8%, WavLM resulted in an EER of 10.88%. The capability of speaker embedding models to generate distinctly separable embeddings plays a crucial role in a speaker verification system.  
A critical aspect of our study was the application of large models to small datasets, specifically for testing purposes. This approach, not intended for training, was instrumental in assessing the models' efficiency and accuracy in real-world scenarios where data availability (of both language-related datasets and particular speaker samples) can be limited.  
The model with the shortest per-sample inference time was Pyannote (49.44 ms), while the longest one was the TitaNet (110.18 ms). Notably, the Ecapa model provides the best combination of short inference time (69.43 ms), which is a little bit longer than PyAnnote, coupled with superior accuracy as reflected by the lowest Equal Error Rate among the models evaluated. This balance is especially relevant in the context of applying large models to smaller datasets for testing, demonstrating the Ecapa model's adaptability and efficiency in varied data environments.  
Conceptualization, V.B., Y.K. and D.S.; methodology, V.B. and D.S.; software, V.B. and M.K.; validation, Y.K., D.S., V.K. and A.K.; formal analysis, Y.K., V.K. and D.S.; investigation, V.B., Y.K. and D.S.; resources, V.B. and D.S.; data curation, V.B. and M.K.; writing--original draft preparation, V.B., D.S. and M.P.; writing--review and editing, M.P.; visualization, V.B. and D.S.; supervision, V.B., Y.K. and D.S.; project administration, Y.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset created, labeled and used within this paper is made available as open source on the HuggingFace repository, at [[https://github.com/vbrjdk/speaker-verification-test](https://github.com/vbrjdk/speaker-verification-test)]([https://github.com/vbrjdk/speaker-verification-test](https://github.com/vbrjdk/speaker-verification-test)), with DOI: [[https://huggingface.co/datasets?other=doi?3A10.57967%2](https://huggingface.co/datasets?other=doi?3A10.57967%2) Fh%2F0701]([https://huggingface.co/datasets?other=doi?3A10.57967%2](https://huggingface.co/datasets?other=doi?3A10.57967%2) Fh%2F0701) (accessed on 6 June 2023).  
Authors [PERSON], [PERSON] and [PERSON] were affiliated by the company Vidby AG. All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.