A number of studies have successfully developed speaker verification or presentation attack detection systems. However, studies integrating the two tasks remain in the preliminary stages. In this paper, we propose two approaches for building an integrated system of speaker verification and presentation attack detection: an end-to-end monolithic approach and a back-end modular approach. The first approach simultaneously trains speaker identification, presentation attack detection, and the integrated system using multi-task learning using a common feature. However, through experiments, we hypothesize that the information required for performing speaker verification and presentation attack detection might differ because speaker verification systems try to remove device-specific information from speaker embeddings, while presentation attack detection systems exploit such information. Therefore, we propose a back-end modular approach using a separate deep neural network (DNN) for speaker verification and presentation attack detection. This approach has thee input components: two speaker embeddings (for enrollment and test each) and prediction of presentation attacks. Experiments are conducted using the ASVspoof 2017-v2 dataset, which includes official trials on the integration of speaker verification and presentation attack detection. The proposed back-end approach demonstrates a relative improvement of 21.77% in terms of the equal error rate for integrated trials compared to a conventional speaker verification system.

Acoustic scene classification contains frequently misclassified pairs of classes that share many common acoustic properties. Specific details can provide vital clues for distinguishing such pairs of classes. However, these details are generally not noticeable and are hard to generalize for different data distributions. In this study, we investigate various methods for capturing discriminative information and simultaneously improve the generalization ability. We adopt a max feature map method that replaces conventional non-linear activation functions in deep neural networks; therefore, we apply an element-wise comparison between the different filters of a convolution layer’s output. Two data augmentation methods and two deep architecture modules are further explored to reduce overfitting and sustain the system’s discriminative power. Various experiments are conducted using the “detection and classification of acoustic scenes and events 2020 task1-a” dataset to validate the proposed methods. Our results show that the proposed system consistently outperforms the baseline, where the proposed system demonstrates an accuracy of 70.4% compared to the baseline at 65.1%.

Speaker verification (SV) technology has the potential to enhance personalization and security in various applications, such as voice assistants, forensics, and access control. However, several challenges hinder the practical application of SV systems, including limitations and distortions in speaker information due to short utterances and noisy environments. Furthermore, these two factors often coexist in real-world situations, resulting in a significant performance degradation of SV systems. Despite the significance of these obstacles, each factor is independently studied, and the co-occurrence of both factors is rarely investigated. Here, we propose a novel SV framework, feature aggregated extended U-Net (FA-ExU-Net), which simultaneously addresses both the challenges by building on the success of prior research on each factor. The FA-ExU-Net incorporates an iterative and hierarchical feature aggregation scheme, a target task-specific feature enhancement module, and a multi-scale feature aggregator for extracting information-rich embeddings. Our proposed system outperforms the recent baseline models based on four evaluation criteria: generalizability, short utterance performance, capacity to handle noisy environments, and robustness to short utterances in noisy environments. We demonstrate the effectiveness of the proposed model through comparison and ablation experiments and intuitive visualizations. The proposed novel approach is expected to contribute to the development of more robust and accurate SV models for practical applications. Our training codes are available at https://github.com/wngh1187/FA-ExU-Net.

The majority of recent speaker verification tasks are studied under open-set evaluation scenarios considering real-world conditions. The characteristics of these tasks imply that the generalization towards unseen speakers is a critical capability. Thus, this study aims to improve the generalization of the system for the performance enhancement of speaker verification. To achieve this goal, we propose a novel supervised-learning-method-based speaker verification system using the mean teacher framework. The mean teacher network refers to the temporal averaging of deep neural network parameters, which can produce a more accurate, stable representations than fixed weights at the end of training and is conventionally used for semi-supervised learning. Leveraging the success of the mean teacher framework in many studies, the proposed supervised learning method exploits the mean teacher network as an auxiliary model for better training of the main model, the student network. By learning the reliable intermediate representations derived from the mean teacher network as well as one-hot speaker labels, the student network is encouraged to explore more discriminative embedding spaces. The experimental results demonstrate that the proposed method relatively reduces the equal error rate by 11.61%, compared to the baseline system.

Background noise reduces speech intelligibility and quality, making speaker verification (SV) in noisy environments a challenging task. To improve the noise robustness of SV systems, additive noise data augmentation method has been commonly used. In this paper, we propose a new additive noise method, partial additive speech (PAS), which aims to train SV systems to be less affected by noisy environments. The experimental results demonstrate that PAS outperforms traditional additive noise in terms of equal error rates (EER), with relative improvements of 4.64% and 5.01% observed in SE-ResNet34 and ECAPA-TDNN. We also show the effectiveness of proposed method by analyzing attention modules and visualizing speaker embeddings.