2023年-2026年：NSFC面上项目“基于自监督预训练模型的异常声音检测”，项目主持。

2021年-2023年：工信部人工智能产业创新任务揭榜挂帅项目“人工智能训练资源库”，课题主持。

2019年-2022年：NSFC联合重点项目“复杂环境下语音数据的说话人识别及关键词检索”，项目主持。

2019年-2023年：国家重点研发计划重点专项课题“基于语音信息的分析”，课题主持。

2019年-2021年：教育部项目“人工智能安全应用及人工智能安全防护关键技术研究”，课题主持。

2014年-2017年：NSFC面上项目“噪声和短语音条件下的说话人识别”，项目主持。

2011年-2013年：NSFC青年项目“面向海量语音信息处理的垃圾过滤和数据选择方法研究”，项目主持。

2010年-2013年：NSFC重大研究计划重点支持项目“多人多方对话中的语音分离、内容分析与理解”，项目参与。

2009年-2011年：国家863重点项目“陪护机器人”，项目参与。

N. Si, H. Zhang, W. Zhang, W.-Q. Zhang, H. Chang, and D. Qu, “Gradient-aware knowledge distillation: Tackling gradient insensitivity through teacher guided gradient scaling,” Neural Networks, vol. 195, Art. no. 108229, Mar. 2026. doi: 10.1016/j.neunet.2025.108229.

W.-Q. Zhang, “Accelerating Cross-correlation for long sequences with short lag constraints: An optimized block-wise approach,” Digital Signal Processing, vol. 168, Art. no. 105509, Jan. 2026. doi: 10.1016/j.dsp.2025.105509.

B. Han, A. Jiang, X. Zheng, W.-Q. Zhang, J. Liu, P. Fan, and Y. Qian, “Exploring self-supervised audio models for generalized anomalous sound detection,” IEEE Transactions on Audio, Speech and Language Processing, vol. 33, pp. 4126-4141, 2025. doi: 10.1109/TASLPRO.2025.3606200.

J. Du, J. Li, G. Chen, and W.-Q. Zhang, “SpeechColab leaderboard: An open-source platform for automatic speech recognition evaluation,” Computer Speech & Language, vol. 94, Art. no. 101805, Nov. 2025. doi: 10.1016/j.csl.2025.101805.

Y.-F. Shao, F. Guo, P. Jiang, W. Li, and W.-Q. Zhang, “Damage detection and classification of carbon fiber-reinforced polymer composite materials based on acoustic emission and convolutional recurrent neural network,” Structural Health Monitoring, vol. 24, no. 6, pp. 3344-3362, Nov. 2025. doi: 10.1177/14759217241270883.

H. Wang and W.-Q. Zhang, “Unstructured pruning and low rank factorisation of self-supervised pre-trained speech models,” IEEE Journal of Selected Topics in Signal Processing, vol. 18, no. 6, pp. 1046–1058, Sept. 2024. doi: 10.1109/JSTSP.2024.3433616.

Y.-F. Shao, P. Jiang, Y. Dong, W. Li, and W.-Q. Zhang, “AE-IRMamba: Low complexity inverted residual Mamba for identification of piezoelectric ceramic and optical fiber acoustic emission sensors signals,” IEEE Sensors Journal, vol. 21, no. 21, pp. 34549–34560, Nov. 2024. doi: 10.1109/JSEN.2024.3457913.

H. Zhang, N. Si, Y. Chen, X. Yang, D. Qu, and W.-Q. Zhang, “Improving speech translation by cross-modal multi-grained contrastive learning,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 1075–1086, Feb. 2023. doi: 10.1109/TASLP.2023.3244521.

X. Chen, J. Wang, X.-L. Zhang, W.-Q. Zhang, and K. Yang, “LMD: A learnable mask network to detect adversarial examples for speaker verification,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 2476–2490, Jun. 2023. doi: 10.1109/TASLP.2023.3288417.

J. Yao, X. Chen, X.-L. Zhang, W.-Q. Zhang, and K. Yang, “Symmetric saliency-based adversarial attack to speaker identification,” IEEE Signal Processing Letters, vol. 30, 2023. doi: 10.1109/LSP.2023.3236509.

Y. Qin, L. Sun, H. Chen, W. Yang, W.-Q. Zhang, J. Fei, and G. Wang, “MVKT-ECG: Efficient single-lead ECG classification for multi-label arrhythmia by multi-view knowledge transferring,” Computers in Biology and Medicine, vol. 166, Art. no. 107503, Sept. 2023. doi: 10.1016/j.compbiomed.2023.107503.

C. Wu, F. Wu, T. Qi, W.-Q. Zhang, X. Xie, and Y. Huang, “Removing AI’s sentiment manipulation of personalized news delivery,” Humanities & Social Sciences Communications, vol. 9, Art. no. 459, 2022. doi: 10.1057/s41599-022-01473-1.

J. Zhao and W.-Q. Zhang, “Improving automatic speech recognition performance for low-resource languages with self-supervised models,” IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 6, pp. 1227–1241, Oct. 2022. doi: 10.1109/JSTSP.2022.3184480.

Z. Zhao and W.-Q. Zhang, “End-to-end keyword search system based on attention mechanism and energy scorer for low resource languages,” Neural Networks, vol. 139, pp. 326-334, Jul. 2021. doi: 10.1016/j.neunet.2021.04.002.

C. Lu, Y. Liu, W.-Q. Zhang and S. Zhang, “Tightness of a new and enhanced semidefinite relaxation for MIMO detection,” SIAM Journal on Optimization, vol. 29, no. 1, pp. 719-742, Jan. 2019. doi: 10.1137/17M115075X.

J. Kang, W.-Q. Zhang, W.-W. Liu, J. Liu, and M. T. Johnson, “Lattice based transcription loss for end-to-end speech recognition,” Journal of Signal Processing Systems, vol. 90, no. 7, pp. 1013-1023, Sept. 2018. doi: 10.1007/s11265-017-1292-0.

C. Lu, Z. Deng, W.-Q. Zhang, and S.-C. Fang, “Argument division based branch-and-bound algorithm for unit-modulus constrained complex quadratic programming,” Journal of Global Optimization, vol. 70, no. 1, pp. 171-187, Jan. 2018. doi: 10.1007/s10898-017-0551-8.

X.-K. Yang, L. He, D. Qu, and W.-Q. Zhang, “Semi-supervised minimum redundancy maximum relevance feature selection for audio classification,” Multimedia Tools and Applications, vol. 77, pp. 713-739, Jan. 2018. doi: 10.1007/s11042-016-4287-0.

X. Yang, D. Qu, W.-L. Zhang, and W.-Q. Zhang, “An adapted data selection for deep learning-based audio segmentation in multi-genre broadcast channel,” Digital Signal Processing, vol. 81, pp. 8-15, Oct. 2018. doi: 10.1016/j.dsp.2018.03.004.

W.-W. Liu, M. Cai, W.-Q. Zhang, J. Liu, and M. T. Johnson, “Discriminative boosting algorithm for diversified front-end phonotactic language recognition,” Journal of Signal Processing Systems, vol. 82, no. 2, pp. 229-239, Feb. 2016. doi: 10.1007/s11265-015-1017-1.

W.-Q. Zhang, “Fast Doppler rate estimation based on fourth-order moment spectrum,” Electronics Letters, vol. 51, no. 23, pp. 1926–1928, Nov. 2015. doi: 10.1049/el.2015.2182.

Z.-Y. Li, W.-Q. Zhang, and J. Liu, “Multi-resolution time frequency feature and complementary combination for short utterance speaker recognition,” Multimedia Tools and Applications, vol. 74, pp. 937-953, Feb. 2015. doi: 10.1007/s11042-013-1705-4.

W.-Q. Zhang, W.-W. Liu, Z.-Y. Li, Y.-Z. Shi, and J. Liu, “Spoken language recognition based on gap-weighted subsequence kernels,” Speech Communication, vol. 60, pp. 1-12, May 2014. doi: 10.1016/j.specom.2014.01.005.

Y.-Z. Shi, W.-Q. Zhang, J. Liu, and M. Johnson, “Efficient one-pass decoding with NNLM for speech recognition,” IEEE Signal Processing Letters, vol. 21, no. 4, pp. 377-381, Apr. 2014. doi: 10.1109/LSP.2014.2303136.

W.-L. Zhang, D. Qu, W.-Q. Zhang, and B.-C. Li. “Rapid speaker adaptation using compressive sensing,” Speech Communication, vol. 55, no. 10, pp. 950-963, Nov.-Dec. 2013. doi: 10.1016/j.specom.2013.06.012.

W.-L. Zhang, W.-Q. Zhang, B.-C. Li, D. Qu, and M. T. Johnson, “Bayesian speaker adaptation based on a new hierarchical probabilistic model,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, no. 7, pp. 2002-2015, Sept. 2012. doi: 10.1109/TASL.2012.2193390.

W.-Q. Zhang, L. He, Y. Deng, J. Liu, and M. Johnson, “Time-frequency cepstral feature and constrained heteroscedastic linear discriminant analysis for language recognition,” IEEE Transactions on Audio, Speech and Language Processing, vol. 19, no. 2, pp. 266-272, Feb. 2011. doi: 10.1109/TASL.2010.2047680.

W.-Q. Zhang, T. Hou, and J. Liu, “Discriminative score fusion for language identification,” Chinese Journal of Electronics, vol. 19, no. 1, pp. 124–128, Jan. 2010. doi: 10.23919/CJE.2010.10159256.

W.-Q. Zhang and J. Liu, “An equalized heteroscedastic linear discriminant analysis algorithm,” IEEE Signal Processing Letters, vol. 15, pp. 585-588, 2008. doi: 10.1109/LSP.2008.2001561.

R. Tao, W.-Q. Zhang, and E.-Q. Chen, “Two-stage method for joint time delay and Doppler shift estimation,” IET Radar, Sonar & Navigation, vol. 2, no. 1, pp. 71-77, Feb. 2008. doi: 10.1049/iet-rsn:20060014.

R. Tao, B. Deng, W.-Q. Zhang, and Y. Wang, “Sampling and sampling rate conversion of band limited signals in the fractional Fourier transform domain,” IEEE Transactions on Signal Processing, vol. 56, no. 1, pp. 158-171, Jan. 2008. doi: 10.1109/TSP.2007.901666.

Z. Weng, D. Shen, T. Liu, G. Chen, R. Shi, J. Chen, C. Ding, W.-Q. Zhang, and Z. Chen, “VocalRep: Structure-aware vocal representations for multimodal generation,” to be published in Proc. ACL, 2026.

S. Yan, Y. Chen, R. Zhou, Z. Yao, S. Chen, T. Zhang, S. Zhang, W.-Q. Zhang, Y. Huang, H. Duan, and Y. Zhang, “Explore-on-Graph: Incentivizing autonomous exploration of large language models on knowledge graphs with path-refined reward modeling,” to be published in Proc. ICLR, 2026.

G. Lin, Z. Chen, Y. Fu, K. Li, and W.-Q. Zhang, “Enhancing multilingual LLM-based ASR with mixture of experts and dynamic downsampling,”  to be published in Proc. ICASSP, 2026.

W. Liang, Y. Qiu, A. Jiang, B. Han, T. Liu, X. Zheng, P. Fan, C. Lu, J. Liu, and W.-Q. Zhang, “RefGen: Reference-guided synthetic data generation for anomalous sound detection,” to be published in Proc. ICASSP, 2026.

J. Fan, W. Liang, and W.-Q. Zhang, “SARNet: A spike-aware consecutive validation framework for accurate remaining useful life prediction,” to be published in Proc. ICASSP, 2026.

R. Bao, H. Ma, S. Liu, C. Gong, C. Zhang, X.-L. Zhang, W.-Q. Zhang, and X. Li, “ALMA-Chor: Leveraging audio-lyric alignment with mamba for chorus detection,” to be published in Proc. ICASSP, 2026.

Y. Yang, Z. Song, J. Zhuo, M. Cui, J. Li, B. Yang, Y. Du, Z. Ma, X. Liu, Z. Wang, K. Li, S. Fan, K. Yu, W.-Q. Zhang, G. Chen, and X. Chen, “GigaSpeech 2: An evolving, large-scale and multi-domain ASR corpus for low-resource languages with automated crawling, transcription and refinement,” in Proc. ACL, 2025, pp. 2673–2686.  doi: 10.18653/v1/2025.acl-long.135.

Y. Pu and W.-Q. Zhang, “Integrating pause information with word embeddings in language models for Alzheimer’s disease detection from spontaneous speech,” in Proc. ICASSP, 2025. doi: 10.1109/ICASSP49660.2025.10888563.

Z. Wan, Z. Qiu, Y. Liu, and W.-Q. Zhang, “Metadata-enhanced speech emotion recognition: Augmented residual integration and co-attention in two-stage fine-tuning,” in Proc. ICASSP, 2025. doi: 10.1109/ICASSP49660.2025.10890812.

Z. Chen, Y.-F. Shao, Y. Ma, M. Wei, L. Zhang, and W.-Q. Zhang, “Improving acoustic scene classification in low-resource conditions,” in Proc. ICASSP, 2025. doi: 10.1109/ICASSP49660.2025.10888928.

A. Jiang, X. Zheng, B. Han, Y. Qiu, P. Fan, W.-Q. Zhang, L. Cheng, and J. Liu, “Adaptive prototype learning for anomalous sound detection with partially known attributes,” in Proc. ICASSP, 2025. doi: 10.1109/ICASSP49660.2025.10889514.

B. Han, W. Huang, Z. Chen, A. Jiang, P. Fan, L. Cheng, Z. Lv, J. Liu, W.-Q. Zhang, and Y. Qian, “Data-efficient low-complexity acoustic scene classification via distilling and progressive pruning,” in Proc. ICASSP, 2025. doi: 10.1109/ICASSP49660.2025.10890296.

K. Pang, M. Bai, J. Yang, W.-Q. Zhang, M. Jiang, and Y. Huang, “Winstega: An adaptive robust enhancement framework for generative linguistic steganography,” in Proc. ICASSP, 2025. doi: 10.1109/ICASSP49660.2025.10888944.

K. Jia, J. Li, K. Li, and W.-Q. Zhang, “Whisper-based multilingual Alzheimer’s disease detection and improvements for low-resource language,” in Proc. Interspeech, 2025, pp. 549-553. doi: 10.21437/Interspeech.2025-1118.

Q. Sun, Z. Qiu, Y. Pu, J. Li, X. Chen, and W.-Q. Zhang, “PPGs-BERT: Leveraging phoneme sequence and BERT for Alzheimer’s disease detection from spontaneous speech,” in Proc. Interspeech, 2025, pp. 554-558. doi: 10.21437/Interspeech.2025-489.

Y. Pu, X. Liu, G. Zhang, Z. Yan, W.-Q. Zhang, and X. Chen, “Empowering large language models for end-to-end speech translation leveraging synthetic data,” in Proc. Interspeech, 2025, pp. 26-30. doi: 10.21437/Interspeech.2025-2341.

W. Liang, R. Zhang, X. Zhang, Y. Ma, and W.-Q. Zhang, “DepressGEN: Synthetic data generation framework for depression detection,” in Proc. Interspeech, 2025, pp. 464-468. doi: 10.21437/Interspeech.2025-280.

B. Han, Z. Lv, A. Jiang, W. Huang, Z. Chen, Y. Deng, J. Ding, C. Lu, W.-Q. Zhang, P. Fan, J. Liu, and Y. Qian, “Exploring large scale pre-trained models for robust machine anomalous sound detection,” in Proc. ICASSP, 2024, pp. 1327–1330. doi: 10.1109/ICASSP48485.2024.10447183.

J. Li and W.-Q. Zhang, “Whisper-based transfer learning for Alzheimer disease classification: Leveraging speech segments with full transcripts as prompts,” in Proc. ICASSP, 2024, pp. 11211–11215. doi: 10.1109/ICASSP48485.2024.10448004.

H. Wang, G. Hu, G. Lin, W.-Q. Zhang, and J. Li, “Simul-Whisper: Attention-guided streaming Whisper with truncation detection,” in Proc. Interspeech, 2024, pp. 4483–4487. doi: 10.21437/Interspeech.2024-1814.

J. Li, Y. Pu, Q. Sun, and W.-Q. Zhang, “Improving Whisper’s recognition performance for under-represented language Kazakh leveraging unpaired speech and text,” in Proc. Interspeech, 2024, pp. 2514–2518. doi: 10.21437/Interspeech.2024-1790.

A. Jiang, B. Han, Z. Lv, Y. Deng, W.-Q. Zhang, X. Chen, Y. Qian, J. Liu, and P. Fan, “AnoPatch: Towards better consistency in machine anomalous sound detection,” in Proc. Interspeech, 2024, pp. 107–111. doi: 10.21437/Interspeech.2024-1761.

X. Zheng, A. Jiang, B. Han, Y. Qian, P. Fan, J. Liu, and W.-Q. Zhang, “Improving anomalous sound detection via low-rank adaptation fine-tuning of pre-trained audio models,” in Proc. SLT, 2024, pp. 979–984. doi: 10.1109/SLT61566.2024.10832335.

A. Jiang, Y. Shi, P. Fan, W.-Q. Zhang, and J. Liu, “CoopASD: Cooperative machine anomalous sound detection with privacy concerns,” in Proc. GLOBECOM, 2024, pp. 346–351. doi: 10.1109/GLOBECOM52923.2024.10901774.

X. Chen, Y. Pu, J. Li, and W.-Q. Zhang, “Cross-lingual Alzheimer’s disease detection based on paralinguistic and pre-trained features,” in Proc. ICASSP, 2023. doi: 10.1109/ICASSP49357.2023.10095522.

A. Jiang, W.-Q. Zhang, Y. Deng, P. Fan, and J. Liu, “Unsupervised anomaly detection and localization of machine audio: A GAN-based approach,” in Proc. ICASSP, 2023. doi: 10.1109/ICASSP49357.2023.10096813.

H. Wang, S. Wang, W.-Q. Zhang, and J. Bai, “DistilXLSR: A light weight cross-lingual speech representation model,” in Proc. Interspeech, 2023, pp. 2273–2277. doi: 10.21437/Interspeech.2023-1444.

H. Wang, S. Wang, W.-Q. Zhang, H. Suo, and Y. Wan, “Task-agnostic structured pruning of speech representation models,” in Proc. Interspeech, 2023, pp. 231–235. doi: 10.21437/Interspeech.2023-1442.

Z. Cui, W. Wu, C. Zhang, W.-Q. Zhang, and J. Wu, “Transferring speech-generic and depression-specific knowledge for Alzheimer’s disease detection,” in Proc. ASRU, 2023. doi: 10.1109/ASRU57964.2023.10389785.

Y. Wang, C. Tang, Z. Ma, Z. Zheng, X. Chen, and W.-Q. Zhang, “Exploring effective distillation of self-supervised speech models for automatic speech recognition,” in Proc. ASRU, 2023. doi: 10.1109/ASRU57964.2023.10389746.

Q. Hou, A. Jiang, W.-Q. Zhang, P. Fan, and J. Liu, “Decoupling detectors for scalable anomaly detection in AIoT systems with multiple machines,” in Proc. GLOBECOM, 2023, pp. 5943–5948. doi: 10.1109/GLOBECOM54140.2023.10436800.

J. Zhao, H. Wang, J. Li, S. Chai, G. Wang, G. Chen, and W.-Q. Zhang, “The THUEE system description for the IARPA OpenASR21 challenge,” in Proc. Interspeech, 2022. doi: 10.21437/Interspeech.2022-269.

J. Zhao, G. Shi, G.-B. Wang, and W.-Q. Zhang, “Automatic speech recognition for low-resource languages: The THUEE systems for the IARPA OpenASR20 evaluation,” in Proc. ASRU, 2021, pp. 335–341. doi: 10.1109/ASRU51503.2021.9688260.

L. Xue, K. Song, D. Wu, X. Tan, N. L. Zhang, T. Qin, W.-Q. Zhang, and T.-Y. Liu, “DeepRapper: Neural rap generation with rhyme and rhythm modeling,” in Proc. ACL, 2021, pp. 69-81. doi: 10.18653/v1/2021.acl-long.6.

G. Chen, S. Chai, G. Wang, J. Du, W.-Q. Zhang, C. Weng, D. Su, D. Povey, J. Trmal, J. Zhang, M. Jin, S. Khudanpur, S. Watanabe, S. Zhao, W. Zou, X. Li, X. Yao, Y. Wang, Y. Wang, Z. You, and Z. Yan, “GigaSpeech: An evolving, multi-domain ASR corpus with 10,000 hours of transcribed audio,” in Proc. Interspeech, 2021, pp. 3670-3674. doi: 10.21437/Interspeech.2021-1965.

J. Zhao, Z. Lv, A. Han, G. Wang, G. Shi, J. Kang, J. Yan, P. Hu, S. Huang, and W.-Q. Zhang, “The TNT team system descriptions of Cantonese and Mongolian for IARPA OpenASR20,” in Proc. Interspeech, 2021, pp. 4344-4348. doi: 10.21437/Interspeech.2021-1063.

H. Yu, J. Zhao, S. Yang, Z. Wu, Y. Nie, and W.-Q. Zhang, “Language recognition based on unsupervised pretrained models,” in Proc. Interspeech, 2021, pp. 3271-3275. doi: 10.21437/Interspeech.2021-807.

Y. Yan, X. Tan, B. Li, G. Zhang, T. Qin, S. Zhao, Y. Shen, W.-Q. Zhang, and T.-Y. Liu, “Adaptive text to speech for spontaneous style,” in Proc. Interspeech, 2021, pp. 4668-4672. doi: 10.21437/Interspeech.2021-584.

K. He, Y. Shen, W.-Q. Zhang, and J. Liu, “Staged training strategy and multi-activation for audio tagging with noisy and sparse multi-label data,” in Proc. ICASSP, 2020, pp. 631-635. doi: 10.1109/ICASSP40776.2020.9053776.

J. Xie, R. Yan, S. Xiao, L. Peng, M. T. Johnson, and W.-Q. Zhang, “Dynamic temporal residual learning for speech recognition,” in Proc. ICASSP, 2020, pp. 7709-7713. doi: 10.1109/ICASSP40776.2020.9054653.

Z. Zhao and W.-Q. Zhang, “End-to-end keyword search based on attention and energy scorer for low resource languages,” in Proc. Interspeech, 2020, pp. 2587-2591. doi: 10.21437/Interspeech.2020-2613.

R. Li, T. Liang, D. Song, Y. Liu, Y. Wu, C. Xu, P. Ouyang, X. Zhang, X. Chen, W.-Q. Zhang, S. Yin, and L. He, “THUEE system for NIST SRE19 CTS challenge,” in Proc. Interspeech, 2020, pp. 2232-2236. doi: 10.21437/Interspeech.2020-1245.

Z. Li, L. He, J. Li, L. Wang, and W.-Q. Zhang, “Towards discriminative representations and unbiased predictions: Class-specific angular softmax for speech emotion recognition,” in Proc. Interspeech, 2019, pp. 1696-1700. doi: 10.21437/Interspeech.2019-1683.

K. He, Y. Shen, and W.-Q. Zhang, “Hierarchical pooling structure for weakly labeled sound event detection,” in Proc. Interspeech, 2019, pp. 3624-3628. doi: 10.21437/Interspeech.2019-2049.

H. Yang and W.-Q. Zhang, “Music genre classification using duplicated convolutional layers in neural networks,” in Proc. Interspeech, 2019, pp. 3382-3386. doi: 10.21437/Interspeech.2019-1298.

Y. Shen, K. He, and W.-Q. Zhang, “Learning how to listen: A temporal-frequential attention model for sound event detection,” in Proc. Interspeech, 2019, pp. 2563-2567. doi: 10.21437/Interspeech.2019-2045.

J. Kang, W.-Q. Zhang, and J. Liu, “Gated convolutional networks based hybrid acoustic models for low resource speech recognition,” in Proc. ASRU, 2017, pp. 157-164. doi: 10.1109/ASRU.2017.8268930.

Z.-Q. Lv, J. Kang, W.-Q. Zhang, and J. Liu, “An LSTM-CTC based verification system for proxy-word based OOV keyword search,” in Proc. ICASSP, 2017, pp. 5655-5659. doi: 10.1109/ICASSP.2017.7953239.

Y. Tian, L. He, M. Cai, W.-Q. Zhang, and J. Liu, “Deep neural networks based speaker modeling at different levels of phonetic granularity,” in Proc. ICASSP, 2017, pp. 5440-5444. doi: 10.1109/ICASSP.2017.7953196.

X.-K. Yang, D. Qu, W.-L. Zhang, and W.-Q. Zhang, “The NDSC transcription system for the 2016 multi-genre broadcast challenge,” in Proc. SLT, 2016, pp. 273-278. doi: 10.1109/SLT.2016.7846276.

Z.-Q. Lv, M. Cai, W.-Q. Zhang, and J. Liu, “A novel discriminative score calibration method for keyword search,” in Proc. Interspeech, 2016, pp. 745-749. doi: 10.21437/Interspeech.2016-606.

Y. Tian, M. Cai, H. Liang, W.-Q. Zhang, and J. Liu, “Improving deep neural networks based speaker verification using unlabeled data,” in Proc. Interspeech, 2016, pp. 1863-1867. doi: 10.21437/Interspeech.2016-614.

Z.-Q. Lv, M. Cai, C. Lu, J. Kang, L.-K. Hui, W.-Q. Zhang, and J. Liu, “Improved system fusion for keyword search,” in Proc. ASRU, 2015, pp. 231-236. doi: 10.1109/ASRU.2015.7404799.

M. Cai, Z.-Q. Lv, B.-L. Song, Y.-Z. Shi, W.-L. Wu, C. Lu, W.-Q. Zhang, and J. Liu, “The THUEE system for the OpenKWS14 keyword search evaluation,” in Proc. ICASSP, 2015, pp. 4734-4738. doi: 10.1109/ICASSP.2015.7178869.

J. Kang, C. Lu, M. Cai, W.-Q. Zhang, and J. Liu, “Neuron sparseness versus connection sparseness in deep neural network for large vocabulary speech recognition,” in Proc. ICASSP, 2015, pp. 4954-4958. doi: 10.1109/ICASSP.2015.7178913.

Y.-Z. Shi, W.-Q. Zhang, M. Cai, and J. Liu, “Variance regularization of RNNLM for speech recognition,” in Proc. ICASSP, 2014, pp. 4931-4935. doi: 10.1109/ICASSP.2014.6854532.

W.-W. Liu, W.-Q. Zhang, Y.-Z. Shi, A. Ji, J. Xu, and J. Liu, “Improved phonotactic language recognition based on RNN feature reconstruction,” in Proc. ICASSP, 2014, pp. 5359-5363. doi: 10.1109/ICASSP.2014.6854619.

W.-W. Liu, W.-Q. Zhang, and J. Liu, “Phonotactic language identification based on time-gap-weighted lattice kernels,” in Proc. Interspeech, 2014, pp. 3022-3026. doi: 10.21437/Interspeech.2014-606.

W.-L. Zhang, D. Qu, W.-Q. Zhang, and B.-C. Li, “Speaker adaptation based on sparse and low-rank eigenphone matrix estimation,” in Proc. Interspeech, 2014, pp. 2792-2796. doi: 10.21437/Interspeech.2014-496.

Z.-Y. Li, W.-Q. Zhang, W.-W. Liu, Y. Tian, and J. Liu, “Text-independent speaker verification via state alignment,” in Proc. Odyssey, 2014, pp. 68–72. doi: 10.21437/Odyssey.2014-10.

Y.-Z. Shi, W.-Q. Zhang, M. Cai, and J. Liu, “Temporal kernel neural network language model,” in Proc. ICASSP, 2013, pp. 8247-8251. doi: 10.1109/ICASSP.2013.6639273.

W.-Q. Zhang, Z.-Y. Li, W. Liu, and J. Liu, “THU-EE system fusion for the NIST 2012 speaker recognition evaluation,” in Proc. Interspeech, 2013, pp. 2474-2478. doi: 10.21437/Interspeech.2013-413.

W. Liu, W.-Q. Zhang, Zhang, Z.-Y. Li, and J. Liu. “Parallel absolute-relative feature based phonotactic language recognition,” in Proc. Interspeech, 2013, pp. 59-63. doi: 10.21437/Interspeech.2013-38.

W.-L. Zhang, W.-Q. Zhang, and B.-C. Li, “Compact acoustic modeling based on acoustic manifold using a mixture of factor analyzers,” in Proc. ASRU, 2013, pp. 37-42. doi: 10.1109/ASRU.2013.6707702.

Z.-Y. Li, W.-Q. Zhang, L. He, and J. Liu, “Complementary combination in i-vector level for language recognition,” in Proc. Odyssey, 2012, pp. 334-337. Available: https://www.isca-archive.org/odyssey_2012/li12_odyssey.html

Y.-Z. Shi, W.-Q. Zhang, and J. Liu, “Robust audio fingerprinting based on local spectral luminance maxima scheme,” in Proc. Interspeech, 2011, pp. 2485-2488. doi: 10.21437/Interspeech.2011-636.

W.-L. Zhang, W.-Q. Zhang, and B.-C. Li, “Speaker adaptation based on speaker-dependent eigenphone estimation,” in Proc. ASRU, 2011, pp. 48-52. doi: 10.1109/ASRU.2011.6163904.

W.-Q. Zhang, Y. Deng, L. He, and J. Liu, “Variant time-frequency cepstral features for speaker recognition,” in Proc. Interspeech, 2010, pp. 2122-2125. doi: 10.21437/Interspeech.2010-160.

S. Meng, W.-Q. Zhang, and J. Liu, “Combining Chinese spoken term detection systems via side-information conditioned linear logistic regression,” in Proc. Interspeech, 2010, pp. 685-688. doi: 10.21437/Interspeech.2010-260.

J. Yang, J. Liu, and W.-Q. Zhang, “A fast query by humming system based on notes,” in Proc. Interspeech, 2010, pp. 2898-2901. doi: 10.21437/Interspeech.2010-753.

W.-Q. Zhang, Y. Shan, and J. Liu, “Multiple background models for speaker verification,” in Proc. Odyssey, 2010, pp. 47-51. Available: https://www.isca-archive.org/odyssey_2010/zhang10_odyssey.html

W.-Q. Zhang and J. Liu, “Two-stage method for specific audio retrieval,” in Proc. ICASSP, 2007, pp. IV-85-88. doi: 10.1109/ICASSP.2007.367169.