8 papers · 1 filter
What Do Speech Foundation Models Not Learn About Speech?
Abdul Waheed, Hanin Atwany, Bhiksha Raj +1
Understanding how speech foundation models capture non-verbal cues is crucial for improving their interpretability and adaptability across diverse tasks. In our work, we analyze se…
Improving Speaker Representations Using Contrastive Losses on Multi-scale Features
Satvik Dixit, Massa Baali, Rita Singh +1
Speaker verification systems have seen significant advancements with the introduction of Multi-scale Feature Aggregation (MFA) architectures, such as MFA-Conformer and ECAPA-TDNN.…
Did You Hear That? Introducing AADG: A Framework for Generating Benchmark Data in Audio Anomaly Detection
Ksheeraja Raghavan, Samiran Gode, Ankit Shah +4
We introduce a novel, general-purpose audio generation framework specifically designed for anomaly detection and localization. Unlike existing datasets that predominantly focus on…
PDAF: A Phonetic Debiasing Attention Framework For Speaker Verification
Massa Baali, Abdulhamid Aldoobi, Hira Dhamyal +2
Speaker verification systems are crucial for authenticating identity through voice. Traditionally, these systems focus on comparing feature vectors, overlooking the speech's conten…
Audio Entailment: Assessing Deductive Reasoning for Audio Understanding
Soham Deshmukh, Shuo Han, Hazim Bukhari +4
Recent literature uses language to build foundation models for audio. These Audio-Language Models (ALMs) are trained on a vast number of audio-text pairs and show remarkable perfor…
SELM: Enhancing Speech Emotion Recognition for Out-of-Domain Scenarios
Hazim Bukhari, Soham Deshmukh, Hira Dhamyal +2
Speech Emotion Recognition (SER) has been traditionally formulated as a classification task. However, emotions are generally a spectrum whose distribution varies from situation to…
ControlVAR: Exploring Controllable Visual Autoregressive Modeling
Xiang Li, Kai Qiu, Hao Chen +4
Conditional visual generation has witnessed remarkable progress with the advent of diffusion models (DMs), especially in tasks like control-to-image generation. However, challenges…
-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations
Xiang Li, Kai Qiu, Jinglu Wang +6
Referring perception, which aims at grounding visual objects with multimodal referring guidance, is essential for bridging the gap between humans, who provide instructions, and the…