2026-09-22T06:00:48Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/129636 2026-02-10 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01 The student, Alara Tin, accepted the attached license on 2025-05-06 at 13:04. The student, Alara Tin, submitted this Thesis for approval on 2025-05-06 at 13:22. This Thesis was approved for publication on 2025-05-07 at 15:17. DSpace SAF Submission Ingestion Package generated from Vireo submission #22232 on 2025-10-19 at 19:17:09 Beyond words: Understanding emotional shifts in maternal vocalizations through speech emotion recognition models Tin, Alara 2025-05-07 Hasegawa-Johnson, Mark A. Speech Emotion Recognition Cnn-bilstm Acoustic Features Domain Adaptation Mother-infant Interaction eng Automatic Speech Emotion Recognition (SER) has significant potential to provide insights into our understanding of dyadic communications. This study focuses on maternal vocalizations within mother-infant dyadic interactions, examining how mothers’ happy and neutral emotional tones shift in response to varying infant stress levels. To achieve this, we employ multiple models: our hybrid CNN-BiLSTM architecture, alongside pre-trained transformer-based models such as wav2vec 2.0 and HuBERT. Our evaluation demonstrates that the hybrid model outperforms these transformer-based approaches after fine-tuning, achieving a minimum improvement of 3.94 percentage points in the test accuracy and 11 percentage points in the weighted average of F1 scores in the IDP dataset. Using our fine-tuned model, we analyze maternal vocalizations in different age groups of infants (3, 6, and 9 months) and classify infants into low-, mid-, and high-stress categories based on the Root Mean Square (RMS) energy features of their vocalizations during stress-inducing events. Our findings reveal a moderate effect size (Cohen’s d) of associations between high stress levels and pronounced vocalization changes in mothers of 3-month-olds, more nuanced responses in mothers of 9-month-olds, and a balanced distribution of vocalization shifts in mothers of 6-month-olds. The novel application of SER in mother-infant studies underscores emotional adaptation in maternal vocalizations and its potential to expand analyses to bidirectional influences, providing deeper insights into emotional communication dynamics. 2025-05 Text https://hdl.handle.net/2142/129636 Copyright 2025 Alara Tin Electrical & Computer Eng Electrical & Computer Engr University of Illinois Urbana-Champaign M.S. Thesis