1 citations · 1 across the 6 of their papers we have counts for
6 papers
PTS-SNN: A Prompt-Tuned Temporal Shift Spiking Neural Networks for Efficient Speech Emotion Recognition
Xun Su, Huamin Wang, Qi Zhang
Speech Emotion Recognition (SER) is widely deployed in Human-Computer Interaction, yet the high computational cost of conventional models hinders their implementation on resource-c…
PAM: Prompting Audio-Language Models for Audio Quality Assessment
Soham Deshmukh, Dareen Alharthi, Benjamin Elizalde +5
While audio quality is a key performance metric for various audio processing tasks, including generative modeling, its objective measurement remains a challenge. Audio-Language Mod…
NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant Meeting Transcription
Alon Vinnikov, Amir Ivry, Aviv Hurvitz +16
We introduce the first Natural Office Talkers in Settings of Far-field Audio Recordings (``NOTSOFAR-1'') Challenge alongside datasets and baseline system. The challenge focuses on…
Prompting Audios Using Acoustic Properties For Emotion Representation
Hira Dhamyal, Benjamin Elizalde, Soham Deshmukh +3
Emotions lie on a continuum, but current models treat emotions as a finite valued discrete variable. This representation does not capture the diversity in the expression of emotion…
Training Audio Captioning Models without Audio
Soham Deshmukh, Benjamin Elizalde, Dimitra Emmanouilidou +3
Automated Audio Captioning (AAC) is the task of generating natural language descriptions given an audio stream. A typical AAC system requires manually curated training data of audi…
Natural Language Supervision for General-Purpose Audio Representations
Benjamin Elizalde, Soham Deshmukh, Huaming Wang
Audio-Language models jointly learn multimodal text and audio representations that enable Zero-Shot inference. Models rely on the encoders to create powerful representations of the…