2 citations · 2 across the 5 of their papers we have counts for
5 papers
HyWA: Architecture-Preserving Personalized Voice Activity Detection for Full-Duplex Voice Assistants
Hamed Jafarzadeh Asl, Amin Edraki, Mahsa Ghazvini Nejad +4
Voice activity detection (VAD) serves as an early gate in voice-assistant pipelines for smart devices. Because conventional VADs respond to speech from any speaker, nearby conversa…
MoKA: Mixture of Kronecker Adapters
Mohammadreza Sadeghi, Mahsa Ghazvini Nejad, MirHamed Jafarzadeh Asl +4
Parameter-efficient fine-tuning (PEFT) is essential for reducing the computational overhead of large language models (LLMs). Low-rank family adapters are commonly used to control t…
Tiny Noise-Robust Voice Activity Detector for Voice Assistants
Hamed Jafarzadeh Asl, Mahsa Ghazvini Nejad, Amin Edraki +2
Voice Activity Detection (VAD) in the presence of background noise remains a challenging problem in speech processing. Accurate VAD is essential in automatic speech recognition, vo…
OAC: Output-adaptive Calibration for Accurate Post-training Quantization
Ali Edalati, Alireza Ghaffari, Mahsa Ghazvini Nejad +4
Deployment of Large Language Models (LLMs) has major computational costs, due to their rapidly expanding size. Compression of LLMs reduces the memory footprint, latency, and energy…
Mitigating Outlier Activations in Low-Precision Fine-Tuning of Language Models
Alireza Ghaffari, Justin Yu, Mahsa Ghazvini Nejad +3
Low-precision fine-tuning of language models has gained prominence as a cost-effective and energy-efficient approach to deploying large-scale models in various applications. Howeve…