28 citations · 35 across the 19 of their papers we have counts for
5 papers · 1 filter
TI-ASU: Toward Robust Automatic Speech Understanding through Text-to-speech Imputation Against Missing Speech Modality
Tiantian Feng, Xuan Shi, Rahul Gupta +1
Automatic Speech Understanding (ASU) aims at human-like speech interpretation, providing nuanced intent, emotion, sentiment, and content understanding from speech and language (tex…
The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data
Alice Baird, Rachel Manzelli, Panagiotis Tzirakis +7
The NeurIPS 2023 Machine Learning for Audio Workshop brings together machine learning (ML) experts from various audio domains. There are several valuable audio-driven ML tasks, fro…
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…
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…
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…