1 citations · 1 across the 3 of their papers we have counts for
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Foundation Model Assisted Automatic Speech Emotion Recognition: Transcribing, Annotating, and Augmenting
Tiantian Feng, Shrikanth Narayanan
Significant advances are being made in speech emotion recognition (SER) using deep learning models. Nonetheless, training SER systems remains challenging, requiring both time and c…
MM-AU:Towards Multimodal Understanding of Advertisement Videos
Digbalay Bose, Rajat Hebbar, Tiantian Feng +3
Advertisement videos (ads) play an integral part in the domain of Internet e-commerce as they amplify the reach of particular products to a broad audience or can serve as a medium…
Understanding Spoken Language Development of Children with ASD Using Pre-trained Speech Embeddings
Anfeng Xu, Rajat Hebbar, Rimita Lahiri +5
Speech processing techniques are useful for analyzing speech and language development in children with Autism Spectrum Disorder (ASD), who are often varied and delayed in acquiring…
TrustSER: On the Trustworthiness of Fine-tuning Pre-trained Speech Embeddings For Speech Emotion Recognition
Tiantian Feng, Rajat Hebbar, Shrikanth Narayanan
Recent studies have explored the use of pre-trained embeddings for speech emotion recognition (SER), achieving comparable performance to conventional methods that rely on low-level…
Signal Processing Grand Challenge 2023 -- e-Prevention: Sleep Behavior as an Indicator of Relapses in Psychotic Patients
Kleanthis Avramidis, Kranti Adsul, Digbalay Bose +1
This paper presents the approach and results of USC SAIL's submission to the Signal Processing Grand Challenge 2023 - e-Prevention (Task 2), on detecting relapses in psychotic pati…
Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP
Nikolaos Antoniou, Athanasios Katsamanis, Theodoros Giannakopoulos +1
There is an imminent need for guidelines and standard test sets to allow direct and fair comparisons of speech emotion recognition (SER). While resources, such as the Interactive E…