A new lightweight short speech speaker recognition system, termed Dense-Fusion2Net, improves acoustic feature utilization using time-frequency attention mechanisms. Current speaker recognition systems struggle with short speech segments due to limited data and the challenges posed by noise. Researchers have developed a solution that not only addresses these shortcomings but also enhances speaker recognition accuracy.
The Dense-Fusion2Net architecture is designed to effectively utilize limited acoustic features found in brief speech segments. In collaboration with this architecture, the Time-Frequency Channel Attention (TFCA) mechanism learns the intricate relationships between time and frequency domains, boosting the global feature extraction capabilities of the recognition system.
Validation experiments were conducted using the Voxceleb dataset, a publicly available collection of speaker audio data. The results of these experiments indicated a significant performance improvement compared to existing recognition systems; the newly proposed model exhibited superior robustness in challenging short speech scenarios.
A critical aspect of this research involved experiments to determine the optimal window length for processing short-duration speech. The findings identified a balance that considers both time and frequency resolution, ensuring improved recognition accuracy.
The study's findings have wide-ranging implications for various applications, including security, access control, and voice interaction systems. The ability to quickly and accurately recognize speakers from short speech segments has the potential to enhance user experiences in these fields significantly.
According to the authors of the article, "The experimental results show that our proposed method outperforms current state-of-the-art systems." They further assert, "We have conducted a large number of short speech recognition experiments on the window length of the short-time Fourier transform and found a window length balance point that can simultaneously take into account the time resolution and frequency resolution of short speech segments." This innovation underlines the importance of ongoing research in speaker recognition technologies that can adapt to real-world audio conditions.
The work presented demonstrates that advancements like Dense-Fusion2Net and the associated TFCA not only improve system performance but also pave the way for practical implementations in daily life. The research was supported by the General Program of Sichuan Province, China, under Grant 2024NSFSC0514, showcasing the collaborative efforts in advancing this critical area of technology.