Short Utterance Speaker Recognition Based on Speech High Frequency Information Compensation and Dynamic Feature Enhancement Methods

• Autorzy: Zi Yunfei , Xiong Shengwu

Identyfikatory

DOI

10.24425/aoa.2024.148768

• Języki publikacji: EN

Abstrakty

EN

This work aims to further compensate for the weaknesses of feature sparsity and insufficient discriminative acoustic features in existing short-duration speaker recognition. To address this issue, we propose the Bark-scaled Gauss and the linear filter bank superposition cepstral coefficients (BGLCC), and the multidimensional central difference (MDCD) acoustic feature extracted method. The Bark-scaled Gauss filter bank focuses on low-frequency information, while linear filtering is uniformly distributed, therefore, the filter superposition can obtain more discriminative and richer acoustic features of short-duration audio signals. In addition, the multi-dimensional central difference method captures better dynamics features of speakers for improving the performance of short utterance speaker verification. Extensive experiments are conducted on short-duration text-independent speaker verification datasets generated from the VoxCeleb, SITW, and NIST SRE corpora, respectively, which contain speech samples of diverse lengths, and different scenarios. The results demonstrate that the proposed method outperforms the existing acoustic feature extraction approach by at least 10% in the test set. The ablation experiments further illustrate that our proposed approaches can achieve substantial improvement over prior methods.

Słowa kluczowe

EN

Bark-scaled Gauss; linear filter; filter bank superposition; multi-dimensional central difference; speaker recognition

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2024
• Tom: Vol. 49, nr 1
• Strony: 37--48
• Opis fizyczny: Bibliogr. 31 poz., rys., tab., wyk.

Twórcy

autor

Zi Yunfei

• School of Computer and Artificial Intelligence, Wuhan University of Technology Wuhan, China

• https://orcid.org/0000-0002-4778-7109

autor

Xiong Shengwu

• School of Computer and Artificial Intelligence, Wuhan University of Technology Wuhan, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).