In recent years, speaker verification has primarily performed using deep neural networks that are trained to output embeddings from input features such as spectrograms or Mel-filterbank energies. Studies that design various loss functions, including metric learning have been widely explored. In this study, we propose two end-to-end loss functions for speaker verification using the concept of speaker bases, which are trainable parameters. One loss function is designed to further increase the interspeaker variation, and the other is designed to conduct the identical concept with hard negative mining. Each speaker basis is designed to represent the corresponding speaker in the process of training deep neural networks. In contrast to the conventional loss functions that can consider only a limited number of speakers included in a mini-batch, the proposed loss functions can consider all the speakers in the training set regardless of the mini-batch composition. In particular, the proposed loss functions enable hard negative mining and calculations of betweenspeaker variations with consideration of all speakers. Through experiments on VoxCeleb1 and VoxCeleb2 datasets, we confirmed that the proposed loss functions could supplement conventional softmax and center loss functions.
In this paper, we propose a replay attack spoofing detection system for automatic speaker verification using multitask learning of noise classes. We define the noise that is caused by the replay attack as replay noise. We explore the effectiveness of training a deep neural network simultaneously for replay attack spoofing detection and replay noise classification. The multi-task learning includes classifying the noise of playback devices, recording environments, and recording devices as well as the spoofing detection. Each of the three types of the noise classes also includes a genuine class. The experiment results on the version 1.0 of ASVspoof2017 datasets demonstrate that the performance of our proposed system is improved by 30% relatively on the evaluation set.
We propose an expanded end-to-end DNN architecture for speaker verification based on b-vectors as well as d-vectors. We embedded the components of a speaker verification system such as modeling frame-level features, extracting utterance-level features, dimensionality reduction of utterancelevel features, and trial-level scoring in an expanded end-toend DNN architecture. The main contribution of this paper is that, instead of using DNNs as parts of the system trained independently, we train the whole system jointly with a finetune cost after pre-training each part. The experimental results show that the proposed system outperforms the baseline dvector system and i-vector PLDA system.
With the increasing popularity of automatic speaker verification (ASV), the reliability of ASV systems has also gained importance. ASV is vulnerable to various spoofing attacks, especially replay attacks. Thus, recent public competitions and studies based on spoofing attack detection for ASV have mainly focused on the detection of replay attacks. Generally, replayed speech includes the attributes of one playback and two recording devices: the playback device, the recording device used by the attacker, and the recording device embedded in any system to verify input utterances. Therefore, the main attributes differentiating a replayed speech from the genuine speech are the attributes of the playback and the recording devices used by the attacker. In this paper, we propose a novel replay attack and its defense through observation of the general speech-spoofing process. The proposed attack includes only the attribute of one recording device embedded in an ASV system; genuine speech passes through the recording device only once, and the replayed speech produced for the proposed attack passes through the same recording device twice. Because the proposed attack is feasible, it can be considered a new task for replay countermeasures in the training process in order to develop a robust ASV protection system. The experimental results show that this novel replay attack cannot be detected by several of the existing state-of-the-art replay attack detection systems. Furthermore, the new attack can be detected by the same systems successfully if they are retrained with an appropriate dataset designed for the new task. INDEX TERMS Automatic speaker verification, replay attack, same recording device, spoofing detection.
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