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20202026
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eess.AS2026

Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning

Xulin Fan, Jialu Li, Mohammad Nur Hossain Khan +4

Recent advances in model design and self-supervised audio representations have improved speech and audio understanding, yet infant-centered naturalistic recordings remain challengi…

eess.AS2025

Automated Analysis of Naturalistic Recordings in Early Childhood: Applications, Challenges, and Opportunities

Jialu Li, Marvin Lavechin, Xulin Fan +4

Naturalistic recordings capture audio in real-world environments where participants behave naturally without interference from researchers or experimental protocols. Naturalistic l…

eess.AS2024

Analysis of Self-Supervised Speech Models on Children's Speech and Infant Vocalizations

Jialu Li, Mark Hasegawa-Johnson, Nancy L. McElwain

To understand why self-supervised learning (SSL) models have empirically achieved strong performances on several speech-processing downstream tasks, numerous studies have focused o…

eess.AS2022

Visualizations of Complex Sequences of Family-Infant Vocalizations Using Bag-of-Audio-Words Approach Based on Wav2vec 2.0 Features

Jialu Li, Mark Hasegawa-Johnson, Nancy L. McElwain

In the U.S., approximately 15-17% of children 2-8 years of age are estimated to have at least one diagnosed mental, behavioral or developmental disorder. However, such disorders of…

eess.AS2020

A Comparison Study on Infant-Parent Voice Diarization

Junzhe Zhu, Mark Hasegawa-Johnson, Nancy McElwain

We design a framework for studying prelinguistic child voicefrom 3 to 24 months based on state-of-the-art algorithms in di-arization. Our system consists of a time-invariant featur…