no code implementations • 8 Dec 2021 • Mufan Sang, Haoqi Li, Fang Liu, Andrew O. Arnold, Li Wan
With our strong online data augmentation strategy, the proposed SSReg shows the potential of self-supervised learning without using negative pairs and it can significantly improve the performance of self-supervised speaker representation learning with a simple Siamese network architecture.
no code implementations • 5 Apr 2021 • Haoqi Li, Yelin Kim, Cheng-Hao Kuo, Shrikanth Narayanan
Key challenges in developing generalized automatic emotion recognition systems include scarcity of labeled data and lack of gold-standard references.
no code implementations • 1 Apr 2021 • Haoqi Li, Brian Baucom, Shrikanth Narayanan, Panayiotis Georgiou
In this paper, we exploit the stationary properties of human behavior within an interaction and present a representation learning method to capture behavioral information from speech in an unsupervised way.
no code implementations • 4 Nov 2019 • Haoqi Li, Ming Tu, Jing Huang, Shrikanth Narayanan, Panayiotis Georgiou
In this paper, we propose a machine learning framework to obtain speech emotion representations by limiting the effect of speaker variability in the speech signals.
1 code implementation • 8 Oct 2019 • Haoqi Li, Brian Baucom, Panayiotis Georgiou
Further, we investigate the importance of emotional-context in the expression of behavior by constraining (or not) the neural networks' contextual view of the data.
no code implementations • 2 Aug 2019 • Sandeep Nallan Chakravarthula, Haoqi Li, Shao-Yen Tseng, Maija Reblin, Panayiotis Georgiou
Cancer impacts the quality of life of those diagnosed as well as their spouse caregivers, in addition to potentially influencing their day-to-day behaviors.
1 code implementation • 7 Feb 2018 • Prashanth Gurunath Shivakumar, Haoqi Li, Kevin Knight, Panayiotis Georgiou
In this work we model ASR as a phrase-based noisy transformation channel and propose an error correction system that can learn from the aggregate errors of all the independent modules constituting the ASR and attempt to invert those.
no code implementations • 12 Jan 2017 • Haoqi Li, Brian Baucom, Panayiotis Georgiou
Behavioral annotation using signal processing and machine learning is highly dependent on training data and manual annotations of behavioral labels.
no code implementations • 14 Jun 2016 • Haoqi Li, Brian Baucom, Panayiotis Georgiou
We propose a Sparsely-Connected and Disjointly-Trained DNN (SD-DNN) framework to deal with limited data.