2 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.SD2024★ 1 cited
Explaining Deep Learning Embeddings for Speech Emotion Recognition by Predicting Interpretable Acoustic Features
Satvik Dixit, Daniel M. Low, Gasser Elbanna +2
Pre-trained deep learning embeddings have consistently shown superior performance over handcrafted acoustic features in speech emotion recognition (SER). However, unlike acoustic f…
eess.AS2022★ 2 cited
Efficient Speech Quality Assessment using Self-supervised Framewise Embeddings
Karl El Hajal, Zihan Wu, Neil Scheidwasser-Clow +2
Automatic speech quality assessment is essential for audio researchers, developers, speech and language pathologists, and system quality engineers. The current state-of-the-art sys…