Multi-Fisher and Triple-Domain Feature Enhancement-Based Short Utterance Speaker Verification for IoT Smart Service

Speech authentication in IoT smart services typically involves short utterances. However, due to the short duration of these utterances (e.g., less than 3 s) and the limited enrolment and/or test data available, it is difficult to learn enough information to accurately distinguish the person. As a result, speaker recognition from short utterances is very challenging. In this article, in the acoustic end, we propose a Multi-Fisher feature enhancement method to enrich short utterance effective information. The method aggregates the effective feature parameters by assigning the corresponding Multi-Fisher ratio weights to these parameters. In the architecture end, we propose a triple-domain feature joint learning method to enhance discriminative information from multiple dimensions. This approach provides different dimensions of information through the different physical meanings of speech in the time domain, the frequency domain, and the spectral domain. Extensive experiments were conducted on VoxCeleb, SITW, and NIST SRE short-duration text-independent speaker verification tasks, containing speech samples of varying lengths and scenarios. The results show that our proposed method outperforms existing acoustic feature extraction approaches and state-of-the-art deep learning architectures by at least 6% and 12%, respectively. The ablation experiments further illustrate that our proposed approaches can achieve substantial improvement over previous methods.