###### Abstract  
The development of deep learning greatly promotes the progress of speaker verification (SV). Studies show that both convolutional neural networks (CNNs) and dilated time-delay neural networks (TDNNs) achieve advanced performance in text-independent SV, due to their ability to sufficiently extract the local feature and the temporal contextual information, respectively. Also, the combination of the above two has achieved better results. However, we found a serious gridding effect when we apply the 1D-Res2 Net-based dilated TDNN proposed in ECAPA-TDNN for SV, which indicates discontinuity and local information losses of frame-level features. To achieve high-resolution process for speaker embedding, we improve the CNN-TDNN structure with proposed repeated multi-scale feature fusions. Through the proposed structure, we can effectively improve the channel utilization of TDNN and achieve higher performance under the same TDNN channel. And, unlike previous studies that have all converted CNN features to TDNN features directly, we also studied the latent space transformation between CNN and TDNN to achieve efficient conversion. Our best method obtains 0.72 EER and 0.0672 MinDCF on VoxCeleb-O test set, and the proposed method performs better in cross-domain SV without additional parameters and computational complexity. speaker verification; speaker embedding; repeated multi-scale fusions; dilated convolution; gridding effect +
Footnote †: journal: Medical  
by introducing the TDNN-based model with dilated convolution, propagation, and aggregation strategies. In addition, studies [22; 23; 24] show that CNNs are very suitable for processing the speech spectrogram, which can fully perceive and extract the texture information in different receptive fields. The ResNet backbone network, for example, can build a lightweight, stable, and robust speaker recognition model [23; 25]. Naturally, a combination of the above two achieves better results [26; 27; 28; 24]. In the CNN-TDNN structure, a CNN front-end is added before TDNN to extract enough local information of the input spectrogram. The features are then sent to TDNN blocks to calculate the temporal contextual features. The CNN-TDNN framework is undoubtedly effective, but it always leads to a larger model size and higher calculational load. Motivated by [29], we believe there is still plenty of scope for improvement by introducing high-resolution fusions. Furthermore, residually dilated TDNN networks lead to advanced performance in SV [26; 27; 30] due to their excellent performance in capturing temporal contextual information. The utilization of dilated convolution extends the receptive field of the TDNN layers, enabling dense feature representation without introducing any additional parameters [19], thereby extracting features in a fully temporal resolution until global embedding and achieving high-resolution feature extraction. Compared with the pooling strategy, it also avoids the loss of temporal-frequency information and maintains the resolution of feature maps [31]. However, we find that the discontinuous sampling of dilated TDNN introduces a serious gridding effect [32; 33], which will result in information loss and a decline in the quality of acoustic frame-level features. In this letter, we propose a CNN-TDNN architecture with repeated fusions for SV, and the framework is exhibited in Figure 1. Our contributions mainly include the following four aspects: (1) we propose a competitive structure without additional number of parameters for high-resolution speaker embedding, which means better performance in SV; (2) we search for CNN encoders with temporal-frequency bottlenecks to extract multi-scale features in the time dimension; (3) we study the structures of repeated multi-scale feature fusions (RMSFs) to ensure the high-resolution feature extraction of TDNNs; and (4) we train the parameter weights using English datasets and test them on the CN-Celeb set. The results indicate that our method presents surprise improvement in cross-domain adaption SV tasks.