The time-delay neural network (TDNN) is one of the state-of-the-art models
for text-independent speaker verification. However, it is difficult for
conventional TDNN to capture global context that has been proven critical for
robust speaker representations and long-duration speaker verification in many
recent works. Besides, the common solutions, e.g., self-attention, have
quadratic complexity for input tokens, which makes them computationally
unaffordable when applied to the feature maps with large sizes in TDNN. To
address these issues, we propose the Global Filter for TDNN, which applies
log-linear complexity FFT/IFFT and a set of differentiable frequency-domain
filters to efficiently model the long-term dependencies in speech. Besides, a
dynamic filtering strategy, and a sparse regularization method are specially
designed to enhance the performance of the global filter and prevent it from
overfitting. Furthermore, we construct a dual-stream TDNN (DS-TDNN), which
splits the basic channels for complexity reduction and employs the global
filter to increase recognition performance. Experiments on Voxceleb and SITW
databases show that the DS-TDNN achieves approximate 10% improvement with a
decline over 28% and 15% in complexity and parameters compared with the
ECAPA-TDNN. Besides, it has the best trade-off between efficiency and
effectiveness compared with other popular baseline systems when facing
long-duration speech. Finally, visualizations and a detailed ablation study
further reveal the advantages of the DS-TDNN.

本文提出 Global Filter for TDNN 和 Dual-Stream TDNN 模型，可在语音辨识方面取得显著的效果，同时减少了参数和复杂性。

动态全局滤波器双流时间延迟神经网络在说话人验证中的应用

Dual-stream Time-Delay Neural Network with Dynamic Global Filter for Speaker Verification

This paper introduces StutterNet, a novel deep learning based stuttering
detection capable of detecting and identifying various types of disfluencies.
Most of the existing work in this domain uses automatic speech recognition
(ASR) combined with language models for stuttering detection. Compared to the
existing work, which depends on the ASR module, our method relies solely on the
acoustic signal. We use a time-delay neural network (TDNN) suitable for
capturing contextual aspects of the disfluent utterances. We evaluate our
system on the UCLASS stuttering dataset consisting of more than 100 speakers.
Our method achieves promising results and outperforms the state-of-the-art
residual neural network based method. The number of trainable parameters of the
proposed method is also substantially less due to the parameter sharing scheme
of TDNN.

本文介绍了 StutterNet，一种新颖的基于深度学习的口吃检测方法，仅依赖于声学信号，使用适用于捕获失聚言语语境方面的时延神经网络，并在包含 100 多个说话者的 UCLASS 口吃数据集上验证，结果表明该方法优于现有方法，参数少并且性能良好。