4 citations · 8 across the 9 of their papers we have counts for
10 papers
CoLMbo-SV: A Grounded Language Model for Explainable Speaker Verification
Massa Baali, Sarthak Bisht, Ziyue Qiu +3
Speaker verification systems achieve high accuracy but provide little account of the acoustic evidence behind their judgments. Making these systems inspectable requires exposing in…
aoip.ai: An Open-Source P2P SDK
Joseph Konan, Shikhar Agnihotri, Chia-Chun Hsieh
This white paper introduces aoip.ai, a groundbreaking open-source SDK incorporating peer-to-peer technology and advanced AI integration to transform VoIP and IoT applications. It a…
LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model
Muhammad Ahmed Shah, Roshan Sharma, Hira Dhamyal +10
It has been shown that Large Language Model (LLM) alignments can be circumvented by appending specially crafted attack suffixes with harmful queries to elicit harmful responses. To…
Psychoacoustic Challenges Of Speech Enhancement On VoIP Platforms
Joseph Konan, Shikhar Agnihotri, Ojas Bhargave +4
Within the ambit of VoIP (Voice over Internet Protocol) telecommunications, the complexities introduced by acoustic transformations merit rigorous analysis. This research, rooted i…
Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms
Joseph Konan, Ojas Bhargave, Shikhar Agnihotri +12
In this paper, we present a method for fine-tuning models trained on the Deep Noise Suppression (DNS) 2020 Challenge to improve their performance on Voice over Internet Protocol (V…
PAAPLoss: A Phonetic-Aligned Acoustic Parameter Loss for Speech Enhancement
Muqiao Yang, Joseph Konan, David Bick +5
Despite rapid advancement in recent years, current speech enhancement models often produce speech that differs in perceptual quality from real clean speech. We propose a learning o…
TAPLoss: A Temporal Acoustic Parameter Loss for Speech Enhancement
Yunyang Zeng, Joseph Konan, Shuo Han +5
Speech enhancement models have greatly progressed in recent years, but still show limits in perceptual quality of their speech outputs. We propose an objective for perceptual quali…
Speech Enhancement for Virtual Meetings on Cellular Networks
Hojeong Lee, Minseon Gwak, Kawon Lee +3
We study speech enhancement using deep learning (DL) for virtual meetings on cellular devices, where transmitted speech has background noise and transmission loss that affects spee…
Cellular Network Speech Enhancement: Removing Background and Transmission Noise
Amanda Shu, Hamza Khalid, Haohui Liu +3
The primary objective of speech enhancement is to reduce background noise while preserving the target's speech. A common dilemma occurs when a speaker is confined to a noisy enviro…
Improving Speech Enhancement through Fine-Grained Speech Characteristics
Muqiao Yang, Joseph Konan, David Bick +3
While deep learning based speech enhancement systems have made rapid progress in improving the quality of speech signals, they can still produce outputs that contain artifacts and…