Multi-task learning for text-dependent speaker verification
Text-dependent speaker verification uses short utterances and verifies both speaker identity and text contents. Due to this nature, traditional state-of-the-art speaker verification approaches, such as i-vector, may not work well. Recently, there has been interest of applying deep learning to speaker verification, however in previous works, standalone deep learning systems have not achieved state-of-the-art performance and they have to be used in system combination or as tandem features to obtain gains. In this paper, a novel multi-task deep learning framework is proposed for text-dependent speaker verification. First, multi-task deep learning is employed to learn both speaker identity and text information. With the learned network, utterance level average of the outputs of the last hidden layer, referred to as j-vector, means joint-vector, is extracted. Discriminant function, with classes defined as multi-task labels on both speaker and text, is then applied to the j-vectors as the decision function for the closed-set recognition, and Probabilistic Linear Discriminant Analysis (PLDA), with classes defined as on the multi-task labels, is applied to the j-vectors for the verification. Experiments on the RSR2015 corpus showed that the j-vector approach leads to good result on the evaluation data. The proposed multi-task deep learning system achieved 0.54% EER, 0.14% EER for the closed-set condition.
A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification
In this study, a multi-task deep neural network is proposed for skin lesion analysis. The proposed multi-task learning model solves different tasks (e.g., lesion segmentation and two independent binary lesion classifications) at the same time by exploiting commonalities and differences across tasks. This results in improved learning efficiency and potential prediction accuracy for the task-specific models, when compared to training the individual models separately. The proposed multi-task deep learning model is trained and evaluated on the dermoscopic image sets from the International Skin Imaging Collaboration (ISIC) 2017 Challenge - Skin Lesion Analysis towards Melanoma Detection, which consists of 2000 training samples and 150 evaluation samples. The experimental results show that the proposed multi-task deep learning model achieves promising performances on skin lesion segmentation and classification. The average value of Jaccard index for lesion segmentation is 0.724, while the average values of area under the receiver operating characteristic curve (AUC) on two individual lesion classifications are 0.880 and 0.972, respectively.
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