基于Group-Res2Block的智能合成语音说话人确认方法

doi:10.3969/j.issn.0255-8297.2024.04.012

Abstract

Abstract: The existing speaker verification task is primarily based on natural speech conditions, rendering it unsuitable for intelligent speech synthesis. In response, this paper proposes an intelligent synthetic voice speaker verification method based on Group-Res2Block. Firstly, the Group-Res2Block structure is designed, integrating the current group with adjacent front and rear groups to foster a stronger contextual connection of the speaker’s local characteristics. Secondly, a multi-scale channel attention feature fusion mechanism with parallel structure is designed. This mechanism employs various-sized convolution kernels to select features of the same level in the channel dimension, thereby extracting more expressive speaker features and avoiding information redundancy. Finally, a multi-scale attention feature fusion mechanism of serial structure is designed, and a layer structure is constructed to integrate the deep and shallow features as a whole and give different weights to obtain the optimal feature expression. To verify the effectiveness of the proposed feature extraction network, this paper constructs two kinds of intelligent synthetic speech datasets in Chinese and English. Through ablation and comparative experiments, it is shown that the proposed method outperforms others on evaluation metrics such as accuracy (ACC), equal error rate (EER) and minimum detection cost function (minDCF) for the task. Furthermore, the test results of the generalization performance of the model verify its applicability to unknown intelligent speech algorithms.

Key words: speaker verification, intelligent voice synthesis, Group-Res2Block deep neural network, multi-scale features, attention mechanism

CLC Number:

LI Fei, SU Zhaopin, WANG Niansong, YANG Bo, ZHANG Guofu. Intelligent Synthetic Voice Speaker Verification Method Based on Group-Res2Block[J]. Journal of Applied Sciences, 2024, 42(4): 709-722.

References

[1] Ian B, Michael B, Brett W. Enhancements to DTW and VQ decision algorithms for speaker recognition [J]. Speech Communication, 1993, 13(3/4): 427-433
[2] Gersho A, Gray R M. Vector quantization and signal compression [M]. Berlin: Springer, 1992.
[3] Reynolds D A. Speaker identification and verification using Gaussian mixture speaker models [J]. Speech Communication, 1995, 17(1/2): 91-108.
[4] Reynolds D A, Quatieri T F, Dunn R B. Speaker verification using adapted Gaussian mixture models [J]. Digital Signal Processing, 2000, 10(1/2/3): 19-41.
[5] Huang G, Liu Z, Van L, et al. Densely connected convolutional networks [C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: 2261-2269.
[6] Malykh E, Novoselov S, Kudashev O. On residual CNN in text-dependent speaker verification task [C]//19th International Conference on Speech and Computer (SPECOM), 2017, 10458: 593-601.
[7] Snyder D, Garcia-Romero D, Povey D, et al. Deep neural network embeddings for textindependent speaker verification [C]//Proceedings of the Interspeech 2017, 2017: 999-1003.
[8] 王华朋. 基于深度双向LSTM网络的说话人识别[J]. 计算机工程与设计, 2020, 41(6): 1768-1772. Wang H P. Speaker recognition based on deep bidirectional LSTM network [J]. Computer Engineering and Design, 2020, 41(6): 1768-1772. (in Chinese)
[9] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition [C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 770-778.
[10] Gao S H, Cheng M M, Zhao K, et al. Res2net: a new multi-scale backbone architecture [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(2): 652-662.
[11] Szegedy C. Going deeper with convolutions [C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015: 1-9.
[12] Zhang Y, Lyu Z, Wu H, et al. MFA-conformer: multi-scale feature aggregation conformer for automatic speaker verification [C]//Proceedings of the Interspeech 2022, 2022: 306-310.
[13] Koji O, Takafumi K, Koichi S. Attentive statistics pooling for deep speaker embedding [C]//Proceedings of the Interspeech 2018, 2018: 2252-2256.
[14] Han B, Chen Z, Qian Y. Local information modeling with self-attention for speaker verification [C]//ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing, 2022: 6727-6731.
[15] Woo S, Park J, Lee J, et al. CBAM: convolutional block attention module [C]//15th European Conference on Computer Vision (ECCV), 2018, 11211: 3-19.
[16] Hu J, Shen L, Sun G. Squeeze-and-excitation networks [C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.
[17] Zhao M, Ma Y F, Liu M, et al. The speakIn system for voxCeleb speaker recognition challange 2021[DB/OL]. 2021[2023-02-27]. https://arxiv.org/abs/2109.01989.
[18] Desplanques B, Thienpondt J, Demuynck K. ECAPA-TDNN: emphasized channel attention, propagation and aggregation in TDNN based speaker verification [C]//Proceedings of the Interspeech 2020, 2020: 3830-3834.
[19] Safari P, India M, Hernando J. Self-attention encoding and pooling for speaker recognition [C]//Proceedings of the Interspeech 2020, 2020: 941-945.
[20] Wu Z Z, Kinnunen T, Evans N, et al. ASVspoof 2015: the first automatic speaker verification spoofing and countermeasures challenge [C]//16th Annual Conference of the International Speech Communication Association, 2015: 2037-2041.

Intelligent Synthetic Voice Speaker Verification Method Based on Group-Res2Block

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	JIN Yanliang, CHEN Yantao, GAO Yuan, ZHOU Jiahao. Encrypted Traffic Classification Based on Attention Temporal Convolutional Network [J]. Journal of Applied Sciences, 2024, 42(4): 659-672.
[2]	XIONG Juan, ZHANG Sunjie, KAN Yaya, CHEN Jiahao. Remote Sensing Image Object Detection Based on CAFPN and Refinement Double-Head Decoupling [J]. Journal of Applied Sciences, 2023, 41(6): 989-1003.
[3]	HAN Huiyan, WU Weizhou, WANG Wenjun, HAN Xie. Research on Pipe Sorting Based on Improved Mask R-CNN Using Feature Fusion and Region Generation Network [J]. Journal of Applied Sciences, 2023, 41(5): 840-854.
[4]	ZHANG Chunsen, ZHU Jiangle, ZHANG Xuefen, LIU Xudong, SHI Shu. Void Filling of DEM in a Generative Adversarial Network Fused with Self-Attention Mechanism [J]. Journal of Applied Sciences, 2023, 41(5): 789-800.
[5]	JIA Ning, ZHENG Chunjun. Multi-modal Emotion Recognition Using Speech, Text and Motion [J]. Journal of Applied Sciences, 2023, 41(1): 55-70.
[6]	LIU Fei, LI Xin. Cloud Detection of Landsat Images Based on Attention U-Net [J]. Journal of Applied Sciences, 2022, 40(6): 906-917.
[7]	CHEN Lifang, WEI Mengru. 3D Point Cloud Classification and Segmentation Network Based on Local Feature Enhancement [J]. Journal of Applied Sciences, 2022, 40(2): 328-337.
[8]	LEI Qianhui, PAN Lili, SHAO Weizhi, HU Haipeng, HUANG Yao. Segmentation Model of COVID-19 Lesions Based on Triple Attention Mechanism [J]. Journal of Applied Sciences, 2022, 40(1): 105-115.
[9]	WEI Mingjun, ZHOU Taiyu, JI Zhanlin, ZHANG Xinnan. Mask Wearing Detection in Complex Scenes Based on Mask-YOLO [J]. Journal of Applied Sciences, 2022, 40(1): 93-104.
[10]	FAN Shouxiang, YAO Junping, LI Xiaojun, CHENG Kaiyuan. A Method for Aspect-Level Sentiment Classification Based on Multi-pattern Feature Fusion [J]. Journal of Applied Sciences, 2021, 39(6): 969-982.
[11]	WANG Shengke, REN Pengfei, Lü Xin, ZHUANG Xinfa. Small Target Detection Algorithm of UAV High Resolution Image Based on Center Point and Dual Attention Mechanism [J]. Journal of Applied Sciences, 2021, 39(4): 650-659.
[12]	PENG Ning, CHEN Aibin, ZHOU Guoxiong, CHEN Wenjie, LIU Jing. Environmental Sound Recognition Based on Attention Sinusoidal Representation Network [J]. Journal of Applied Sciences, 2021, 39(4): 641-649.
[13]	ZHAO Yunshan, DUAN Youxiang. Convolutional Neural Networks Text Classification Model Based on Attention Mechanism [J]. Journal of Applied Sciences, 2019, 37(4): 541-550.
[14]	JIN Huazhong, LIU Xiaolong, HU Zike. An Image Caption Generation Model Combining Global and Local Features [J]. Journal of Applied Sciences, 2019, 37(4): 501-509.
[15]	JIA Qi, Lü Xu-liang, WU Chao, TANG Hou-chao. Evaluation of Camouflage Effectiveness Using Human Visual Attention Mechanism [J]. Journal of Applied Sciences, 2011, 29(3): 294-298.