Harnessing the power of Wav2Vec2 and CNNs for Robust Speaker Identification on the VoxCeleb and LibriSpeech Datasets

Abstract

Speaker identification, a cornerstone of speech processing, involves associating individuals with spoken segments within a known speaker pool. This paper presents a significant AI contribution: an innovative framework tailored for closed-set speaker identification. It concurrently emphasizes its practical engineering application in the realm of speech analysis. This paper introduces a pioneering AI framework with substantial neural network architecture enhancements, particularly focusing on optimizing the Log-Softmax function—a linchpin for speaker attribution. Additionally, we seamlessly incorporate cutting-edge data augmentation techniques into the Wav2Vec2 framework. These innovations push the boundaries of current Speaker Identification methodologies. Empirical validation demonstrates our framework’s efficacy, yielding a remarkable relative improvement of up to 3.16% in top-1% accuracy compared to the state-of-the-art. This research sets a new benchmark, surpassing existing standards and unlocking the full potential of closed-set Speaker Identification functions. In addition, the methodology presented in this paper serves as a catalyst for advancing Speaker Identification methodologies in engineering applications, underlining the transformative potential of AI-driven innovations in this domain.