Human Language Technology

Neuromorphic Computing

For many real-world pattern learning and classification tasks, today’s digital computers cannot compete with human brains in terms of energy and computational efficiency. One of the reasons is that the computer architecture is very different from that of our neural systems, in which a huge number of nerve cells communicate with action potentials, which we call ‘spikes’, in parallel. Human brain works with spikes and biological spiking neurons. Spiking neural network is biologically inspired and grounded under a solid scientific framework to achieve some of the advantages of biological systems. It has been a general belief that spiking neural network is the computational way to achieve brain like performance. The research on the theory and implementation of Spiking Neural Network forms the foundation of spiking neural network computing.

Leveraging on the research outcomes in the past decades, this project is focused on developing realistic spiking neural network framework that is hardware implementable by modeling the following brain parts: synapses (plasticity, learning), visual cortex (encoding and recognition), hippocampus (memory and synthesis). The framework will include neural encoding, precise-spike-driven learning for synaptic plasticity learning, neuron modeling for analog memory element characteristics, and classification and synthesis of spatiotemporal patterns.

Project Duration: 31 March 2017 – 30 March 2022

Funding Source: RIE2020 Advanced Manufacturing and Engineering Programmatic Grant A1687b0033

TC1 RRAM Neuromorphic Chip

Acknowledgement: This research work is supported by Programmatic Grant No. A1687b0033 from the Singapore Government’s Research, Innovation and Enterprise 2020 plan (Advanced Manufacturing and Engineering domain). Project Title: Neuromorphic Computing.

PUBLICATIONS

Journal Articles

  • Chenglin Xu, Wei Rao, Eng Siong Chng and Haizhou Li, “SpEx: Multi-Scale Time Domain Speaker Extraction Network”, IEEE/ACM Transaction on Audio, Speech, and Language Processing (2020). [link]
  • Malu Zhang, Xiaoling Luo, Jibin Wu, Yi Chen, Ammar Belatreche, Zihan Pan, Hong Qu, and Haizhou Li, “An Efficient Threshold-Driven Aggregate-Label Learning Algorithm for Multimodal Information Processing,” IEEE Journal of Selected Topics in Signal Processing (2020). [link]
  • Malu Zhang, Jibin Wu, Ammar Belatreche, Zihan Pan, Xiurui Xie, Yansong Chua, Guoqi Li, Hong Qu and Haizhou Li, “Supervised Learning in Spiking Neural Networks with Synaptic Delay-Weight Plasticity,” Neurocomputing (2020). [link]
  • Jibin Wu, Emre Yılmaz, Malu Zhang, Haizhou Li and Kay Chen Tan, “Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition,” Frontiers in Neuroscience, 14(199), March 2020, pp. 1-14. [link]
  • Zihan Pan, Yansong Chua, Jibin Wu, Malu Zhang, Haizhou Li and Eliathamby Ambikairajah, “An Efficient and Perceptually Motivated Auditory Neural Encoding and Decoding Algorithm for Spiking Neural Networks”, Frontiers in Neuroscience, 13(1420), January 2020, pp. 1-17. [link]
  • Yang, Jichen, and Rohan Kumar Das. “Improving anti-spoofing with octave spectrum and short-term spectral statistics information.” Applied Acoustics 157 (2020): 107017. [link]
  • Chong Zhang, Kay Chen Tan, Haizhou Li, and Geok Soon Hong, “A cost-sensitive deep belief network for imbalanced classification,” IEEE Transactions on Neural Networks and Learning Systems, 30(1), January 2019, pp. 1-14. [link]
  • Malu Zhang, Hong Qu, Ammar Belatreche, Yi Chen, and Zhang Yi, “A Highly Effective and Robust Membrane Potential-Driven Supervised Learning Method for Spiking Neurons,” IEEE Transactions on Neural Networks and Learning Systems, 30(1), January 2019, pp. 123-137. [link]
  • Jichen Yang, Rohan Kumar Das and Haizhou Li, “Significance of Subband Features for Synthetic Speech Detection”, IEEE Transactions on Information Forensics and Security, 15(1), December 2019, pp. 2160-2170. [link]
  • Jibin Wu, Yansong Chua, Malu Zhang, Haizhou Li, and Kay Chen Tan, “A Spiking Neural Network Framework for Robust Sound Classification,” Frontiers in Neuroscience, 12, November 2018, p. 836. [link]
  • L. Xu, Kong-Aik Lee, Haizhou Li and Zhen Yang, “Generalizing I-Vector Estimation for Rapid Speaker Recognition”, IEEE/ACM Trans. Audio, Speech & Language Processing, 26(4), April 2018, pp. 749-759. [link]

Conference Articles

  • Xiaohai Tian, Rohan Kumar Das and Haizhou Li, “Black-box Attacks on Automatic Speaker Verification using Feedback-controlled Voice Conversion” in Proc. Speaker Odyssey 2020, Tokyo, Japan, November 2020. [link]
  • Berrak Sisman and Haizhou Li, “Generative Adversarial Networks for Singing Voice Conversion with and without Parallel Data” in Proc. Speaker Odyssey 2020, Tokyo, Japan, November 2020. [link]
  • Kun Zhou, Berrak Sisman and Haizhou Li, “Transforming Spectrum and Prosody for Emotional Voice Conversion with Non-Parallel Training Data” in Proc. Speaker Odyssey 2020, Tokyo, Japan, November 2020. [link]
  • Rui Liu, Berrak Sisman, Feilong Bao, Guanglai Gao and Haizhou Li, “WaveTTS: Tacotron-based TTS with Joint Time-Frequency Domain Loss” in Proc. Speaker Odyssey 2020, Tokyo, Japan, November 2020. [link]
  • Rohan Kumar Das and Haizhou Li “On The Importance of Vocal Tract Constriction for Speaker Characterization: The Whispered Speech Study” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2020, Barcelona, Spain, May 2020. [link]
  • Rohan Kumar Das, Jichen Yang and Haizhou Li “Assessing the Scope of Generalized Countermeasures for Anti-spoofing” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2020, Barcelona, Spain, May 2020. [link]
  • Zhang, Malu, Jibin Wu, Yansong Chua, Xiaoling Luo, Zihan Pan, Dan Liu, and Haizhou Li. “MPD-AL: an efficient membrane potential driven aggregate-label learning algorithm for spiking neurons.” In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1327-1334. 2019. [link]
  • Jibin Wu, Yansong Chua, Malu Zhang, Qu Yang, Guoqi Li and Haizhou Li, “Deep Spiking Neural Network with Novel Spike Count based Learning Rule”, In. Proc. International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July 2019, pp. 1-6. [link]
  • Jibin Wu, Yansong Chua, Malu Zhang and Haizhou Li, “Competitive STDP-based Feature Representation Learning for Sound Event Classification”, In. Proc. International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July 2019, pp. 1-8. [link]
  • Zihan Pan, Jibin Wu, Yansong Chua, Malu Zhang and Haizhou Li, “Neural Population Coding for Effective Temporal Classification”, In. Proc. International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July 2019, pp. 1-8. [link]
  • Rohan Kumar Das, Jichen Yang and Haizhou Li “Long Range Acoustic and Deep Features Perspective on ASV spoof 2019” in Proc. IEEE Automatic Speech Recognition Understanding (ASRU) Workshop 2019, Sentosa Island, Singapore, December 2019. [link]
  • Rohan Kumar Das, Jichen Yang and Haizhou Li “Speaker Clustering with Penalty Distance for Speaker Verification with Multi-Speaker Speech” in Proc. Asia-Pacific Signal and Information Processing Association (APSIPA) Annual Summit and Conference (ASC) 2019, Lanzhou, China, November 2019. [link]
  • Yitong Liu, Rohan Kumar Das and Haizhou Li “Multi-band Spectral Entropy Information for Detection of Replay Attacks” in Proc. Asia-Pacific Signal and Information Processing Association (APSIPA) Annual Summit and Conference (ASC) 2019, Lanzhou, China, November 2019. [link]
  • Rohan Sheelvant, Bidisha Sharma, Maulik Madhavi, Rohan Kumar Das, S. R. M. Prasanna and Haizhou Li “RSL2019: A Realistic Speech Localization Corpus” in Proc. Oriental COCOSDA 2019, Cebu City, Philippines, October 2019. [link]
  • Rohan Kumar Das and Haizhou Li “Instantaneous Phase and Long-term Acoustic Cues for Orca Activity Detection” in Proc. Interspeech 2019, Graz, Austria, September 2019. [link]
  • Rohan Kumar Das, Jichen Yang and Haizhou Li “Long Range Acoustic Features for Spoofed Speech Detection” in Proc. Interspeech 2019, Graz, Austria, September 2019. [link]
  • Bidisha Sharma, Rohan Kumar Das and Haizhou Li “On the Importance of Audio-source Separation for Singer Identification in Polyphonic Music” in Proc. Interspeech 2019, Graz, Austria, September 2019. [link]
  • Tianchi Liu, Maulik Madhavi, Rohan Kumar Das and Haizhou Li “A Unified Framework for Speaker and Utterance Verification” in Proc. Interspeech 2019, Graz, Austria, September 2019. [link]
  • Jibin Wu, Zihan Pan, Malu Zhang, Rohan Kumar Das, Yansong Chua and Haizhou Li “Robust Sound Recognition: A Neuromorphic Approach” in Proc. Interspeech 2019, Graz, Austria, September 2019. [link]
  • Chenglin Xu, Wei Rao, Eng Siong Chng and Haizhou Li, “Time-Domain Speaker Extraction Network”, in Proc. IEEE Automatic Speech Recognition Understanding (ASRU) Workshop 2019, Sentosa Island, Singapore, December 2019. [link]
  • Wei Rao, Chenglin Xu, Eng Siong Chng and Haizhou Li, “Target Speaker Extraction for Multi-Talker Speaker Verification”, in Proc. INTERSPEECH, Graz, Austria, September 2019, pp. 1273-1277. [link]
  • Longting Xu, Rohan Kumar Das, Emre Yılmaz, Jichen Yang and Haizhou Li “Generative x-vectors for text-independent speaker verification” in Proc. IEEE Spoken Language Technology (SLT) 2018, Athens, Greece, December 2018. [link]
  • Rohan Kumar Das and Haizhou Li “Instantaneous Phase and Excitation Source Features for Detection of Replay Attacks” in Proc. Asia-Pacific Signal and Information Processing Association (APSIPA) Annual Summit and Conference (ASC) 2018, Honolulu, Hawaii, USA, November 2018. [link]
  • Rohan Kumar Das, Maulik Madhavi and Haizhou Li “Compensating Utterance Information in Fixed Phrase Speaker Verification” in Proc. Asia-Pacific Signal and Information Processing Association (APSIPA) Annual Summit and Conference (ASC) 2018, Honolulu, Hawaii, USA, November 2018. [link]
  • Jichen Yang, Rohan Kumar Das and Haizhou Li “Extended Constant-Q Cepstral Coefficients for Detection of Spoofing Attacks” in Proc. Asia-Pacific Signal and Information Processing Association (APSIPA) Annual Summit and Conference (ASC) 2018, Honolulu, Hawaii, USA, November 2018. [link]
  • Rohan Kumar Das and S. R. M. Prasanna “Investigating Text-independent Speaker Verification from Practically Realizable System Perspective” in Proc. Asia-Pacific Signal and Information Processing Association (APSIPA) Annual Summit and Conference (ASC) 2018, Honolulu, Hawaii, USA, November 2018. [link]
  • Kantheti Srinivas, Rohan Kumar Das and Hemant A. Patil “Combining Phase-based Features for Replay Spoof Detection” in Proc. International Symposium on Chinese Spoken Language Processing (ISCSLP) 2018, Taipei, Taiwan, November 2018. [link]
  • Jibin Wu, Yansong Chua and Haizhou Li, “A Biologically Plausible Speech Recognition Framework Based on Spiking Neural Networks,” in Proc. International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, July 2018, pp. 1-8. [link]