Deep Learning for Speaker Identification: Architectural Insights from AB-1 Corpus Analysis and Performance Evaluation
<|reference_start|>Deep Learning for Speaker Identification: Architectural Insights from AB-1 Corpus Analysis and Performance Evaluation: In the fields of security systems, forensic investigations, and personalized services, the importance of speech as a fundamental human input outweighs text-based interactions. This research delves deeply into the complex field of Speaker Identification (SID), examining its essential components and emphasising Mel Spectrogram and Mel Frequency Cepstral Coefficients (MFCC) for feature extraction. Moreover, this study evaluates six slightly distinct model architectures using extensive analysis to evaluate their performance, with hyperparameter tuning applied to the best-performing model. This work performs a linguistic analysis to verify accent and gender accuracy, in addition to bias evaluation within the AB-1 Corpus dataset.<|reference_end|>
@article{bartolo2024deep,
    title={Deep Learning for Speaker Identification: Architectural Insights from
  AB-1 Corpus Analysis and Performance Evaluation},
    author={Matthias Bartolo},
    journal={arXiv preprint arXiv:2408.06804},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06804},
    primaryClass={cs.SD cs.AI eess.AS}
}