Search Results for author: Haoqi Li

Found 11 papers, 3 papers with code

The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data

no code implementations21 Mar 2024 Alice Baird, Rachel Manzelli, Panagiotis Tzirakis, Chris Gagne, Haoqi Li, Sadie Allen, Sander Dieleman, Brian Kulis, Shrikanth S. Narayanan, Alan Cowen

In this short white paper, to encourage researchers with limited access to large-datasets, the organizers first outline several open-source datasets that are available to the community, and for the duration of the workshop are making several propriety datasets available.

Event Detection Speech Emotion Recognition

Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Training

1 code implementation22 May 2023 Jianfeng He, Julian Salazar, Kaisheng Yao, Haoqi Li, Jinglun Cai

End-to-end (E2E) spoken language understanding (SLU) is constrained by the cost of collecting speech-semantics pairs, especially when label domains change.

Natural Language Understanding Spoken Language Understanding

Self-Supervised Speaker Verification with Simple Siamese Network and Self-Supervised Regularization

no code implementations8 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.

Contrastive Learning Data Augmentation +3

Acted vs. Improvised: Domain Adaptation for Elicitation Approaches in Audio-Visual Emotion Recognition

no code implementations5 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.

Domain Adaptation Emotion Recognition +1

Unsupervised Speech Representation Learning for Behavior Modeling using Triplet Enhanced Contextualized Networks

no code implementations1 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.

Representation Learning

Speaker-invariant Affective Representation Learning via Adversarial Training

no code implementations4 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.

Emotion Classification Representation Learning +1

Linking emotions to behaviors through deep transfer learning

1 code implementation8 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.

Emotion Recognition Transfer Learning

Predicting Behavior in Cancer-Afflicted Patient and Spouse Interactions using Speech and Language

no code implementations2 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.

Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context Modeling

1 code implementation7 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.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Unsupervised Latent Behavior Manifold Learning from Acoustic Features: audio2behavior

no code implementations12 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.

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