Transformer based on self attention mechanism has demonstrated its state-of-the-art
performance in most natural language processing (NLP) tasks, but it's not very competitive …

Modern speaker verification models use deep neural networks to encode utterance audio
into discriminative embedding vectors. During the training process, these networks are …

As automatic speaker verification (ASV) systems are vulnerable to spoofing attacks, they are
typically used in conjunction with spoofing countermeasure (CM) systems to improve …

This work presents a novel back-end framework for speaker verification using graph
attention networks. Segment-wise speaker embeddings extracted from multiple crops within …

In order to process speech, most state-of-the-art experimental methods employ
convolutional neural networks (CNNs), which operate on a continuous, 1-dimensional (1-D) …

Automatic speaker diarization techniques typically involve a two-stage processing approach
where audio segments of fixed duration are converted to vector representations in the first …

The accuracy of modern automatic speaker verification (ASV) systems, when trained
exclusively on adult data, drops substantially when applied to children's speech. The …

In this paper, we propose a novel algorithm for speaker diarization using metric learning for
graph based clustering. The graph clustering algorithms use an adjacency matrix consisting …

The growing use of voice user interfaces has led to a surge in the collection and storage of
speech data. While data collection allows for the development of efficient tools powering …

Attention mechanism provides an effective and plug-and-play feature enhancement module
for speaker embedding extractors. Attention-based pooling layers have been widely used to …