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<p>In recent years, the realm of audio analysis has undergone notable strides, witnessing transformative progress in processing raw audio signals, and integrating machine learning for sound classification. Nevertheless, prevailing methodologies encounter challenges in attaining both precision and efficiency, necessitating innovative approaches. This study introduces a pioneering methodology tailored for speaker identification and segment attribution within audio recordings. Speaker recognition demands astute discernment of individuals based on their unique vocal characteristics, while diarization endeavors to segment audio accurately to attribute specific segments to respective speakers. Leveraging the OpenAI Whisper model for transcription and the ECAPA-TDNN architecture for speaker embeddings, our methodology employs Agglomerative Hierarchical Clustering to group extracted embeddings. This process unveils intricate speaker relationships and facilitates nuanced segment labeling. This research not only confronts existing challenges but also pioneers a solution that enhances precision and efficacy in speaker recognition and diarization. By pushing the boundaries of conventional practices, our methodology offers practical implications across various applications and heralds new avenues for exploration in the dynamic field of audio analysis.</p>

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Revolutionizing Speaker Recognition and Diarization: A Novel Methodology in Speech Analysis

  • 期刊论文

  • 发表日期：

    2025年1月20日

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  • Ravi D. Shankar,
  • R. B. Manjula,
  • Rajashekhar C. Biradar

摘要

In recent years, the realm of audio analysis has undergone notable strides, witnessing transformative progress in processing raw audio signals, and integrating machine learning for sound classification. Nevertheless, prevailing methodologies encounter challenges in attaining both precision and efficiency, necessitating innovative approaches. This study introduces a pioneering methodology tailored for speaker identification and segment attribution within audio recordings. Speaker recognition demands astute discernment of individuals based on their unique vocal characteristics, while diarization endeavors to segment audio accurately to attribute specific segments to respective speakers. Leveraging the OpenAI Whisper model for transcription and the ECAPA-TDNN architecture for speaker embeddings, our methodology employs Agglomerative Hierarchical Clustering to group extracted embeddings. This process unveils intricate speaker relationships and facilitates nuanced segment labeling. This research not only confronts existing challenges but also pioneers a solution that enhances precision and efficacy in speaker recognition and diarization. By pushing the boundaries of conventional practices, our methodology offers practical implications across various applications and heralds new avenues for exploration in the dynamic field of audio analysis.