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Improving the Dependency on Fine-Tuning Data in Transfer Learning for Speech Emotion Recognition
Ricardo García, Rodrigo Mahu, Carlos Busso +1
Compensating for train-test mismatches in acoustic environments is important for deployable speech solutions. This paper proposes to reduce the amount of fine-tuning data needed in…
Voice Agents under Acoustic Stress: From Signal Degradation to Interaction and Action
Amir Ivry, Kai-Wei Chang, Lin Zhang +2
Voice agents must complete users' tasks despite noise, reverberation, and competing speech. Evaluating agents' robustness therefore requires following how acoustic conditions affec…
MSP-Conversation: A Corpus for Naturalistic, Time-Continuous Emotion Recognition
Luz Martinez-Lucas, Pravin Mote, Abinay Reddy Naini +2
Affective computing aims to understand and model human emotions for computational systems. Within this field, speech emotion recognition (SER) focuses on predicting emotions convey…
Joint Learning using Mixture-of-Expert-Based Representation for Speech Enhancement and Robust Emotion Recognition
Jing-Tong Tzeng, Carlos Busso, Chi-Chun Lee
Speech emotion recognition (SER) plays a critical role in building emotion-aware speech systems, but its performance degrades significantly under noisy conditions. Although speech…