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Qiongqiong Wang

6 accepted papers

2025

Benchmarking Contextual and Paralinguistic Reasoning in Speech-LLMs: A Case Study with In-the-Wild Data

Qiongqiong Wang, Hardik Bhupendra Sailor, Tianchi Liu, Wenyu Zhang, Muhammad Huzaifah, Nattadaporn Lertcheva +4
EMNLP 2025

Recent speech-LLMs have shown impressive performance in tasks like transcription and translation, yet they remain limited in understanding the paralinguistic aspects of speech crucial for social and emotional intelligence. We propose CP-Bench, a benchmark for evaluating speech-LLMs on contextual par

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2023

Disentangling Voice and Content with Self-Supervision for Speaker Recognition

Tianchi Liu, Kong Aik Lee, Qiongqiong Wang, Haizhou Li
NeurIPS 2023poster

For speaker recognition, it is difficult to extract an accurate speaker representation from speech because of its mixture of speaker traits and content. This paper proposes a disentanglement framework that simultaneously models speaker traits and content variability in speech. It is realized with t…

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2023

Incorporating Uncertainty from Speaker Embedding Estimation to Speaker Verification

Qiongqiong Wang, Kong Aik Lee, Tianchi Liu
ICASSP 2023accepted

Speech utterances recorded under differing conditions exhibit varying degrees of confidence in their embedding estimates, i.e., uncertainty, even if they are extracted using the same neural network. This paper aims to incorporate the uncertainty estimate produced in the xi-vector network front-end w…

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2020

A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition

Qiongqiong Wang, Koji Okabe, Kong Aik Lee, Takafumi Koshinaka
ICASSP 2020accepted

This paper proposes a generalized framework for domain adaptation of Probabilistic Linear Discriminant Analysis (PLDA) in speaker recognition. It not only includes several existing supervised and unsupervised domain adaptation methods but also makes possible more flexible usage of available data in…

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2016

Domain adaptation using maximum likelihood linear transformation for PLDA-based speaker verification

Qiongqiong Wang, Hitoshi Yamamoto, Takafumi Koshinaka
ICASSP 2016accepted

While i-vector-PLDA frameworks employing huge amounts of development data have achieved significant success in speaker recognition, it is infeasible to collect a sufficiently large amount of data for every real application. This paper proposes a method to perform supervised domain adaptation of PLDA…

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