TitaNet：基于一维深度可分离卷积和全局上下文的说话人表示神经模型
TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context
摘要：
在本文中，我们提出了TitaNet，一种新的神经网络架构，用于提取说话人表示。我们采用1D深度可分离卷积与具有全局上下文的挤压和激励（SE）层，然后是基于通道注意力的统计池层，以将可变长度的话语映射到固定长度的嵌入（t向量）。TitaNet是一个可扩展的架构，在VoxCeleb 1试验文件上的说话人验证任务中实现了最先进的性能，等错误率（EER）为0.68%，在AMI-MixHeadset上的说话人日志化任务中，日志化错误率（DER）为1.73%，AMI-Lapel上为1.99%，CH 109上为1.11%。此外，我们研究了各种大小的TitaNet，并提出了一个只有6 M参数的轻型TitaNet-S模型，该模型在日志化任务中实现了接近最先进的结果。
英文摘要：
In this paper, we propose TitaNet, a novel neural network architecture for extracting speaker representations. We employ 1D depth-wise separable convolutions with Squeeze-and-Excitation (SE) layers with global context followed by channel attention based statistics pooling layer to map variable-length utterances to a fixed-length embedding (t-vector). TitaNet is a scalable architecture and achieves state-of-the-art performance on speaker verification task with an equal error rate (EER) of 0.68% on the VoxCeleb1 trial file and also on speaker diarization tasks with diarization error rate (DER) of 1.73% on AMI-MixHeadset, 1.99% on AMI-Lapel and 1.11% on CH109. Furthermore, we investigate various sizes of TitaNet and present a light TitaNet-S model with only 6M parameters that achieve near state-of-the-art results in diarization tasks.
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