23 citations · 33 across the 16 of their papers we have counts for
20 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…
FA-Bench: A Benchmark for Phone- and Word-Level Timestamp Accuracy in Forced Alignment and ASR on Clean and Noisy Speech
Wei Chu, Yuanzhe Dong, Ke Tan +11
Forced alignment estimates the timestamps of each word, phone or character in speech given its transcript. Published comparisons normalize transcripts, split the data and match bou…
Inquesto Score: A reliability Protocol For Voice Agents
Massa Baali, Bhiksha Raj
Voice agents are increasingly deployed in workflows where failed interactions can affect transactions, access, and other consequential outcomes, creating a need for reproducible an…
AcoustiClaim: A Numeric Claim Benchmark with Instrument Ground Truth
Sheng-Tse Lin, Siyuan Zhai, Chien-Liang Kuo +2
Audio language models state numbers for acoustic quantities, and neither human opinion nor a judge model says whether such a number is true of the signal. AcoustiClaim extracts eac…
Audio Language Model for Deepfake Detection Grounded in Acoustic Chain-of-Thought
Runkun Chen, Yixiong Fang, Pengyu Chang +3
Deepfake speech detection systems are often limited to binary classification tasks and struggle to generate interpretable reasoning or provide context-rich explanations for their d…
What and When to Learn: CURriculum Ranking Loss for Large-Scale Speaker Verification
Massa Baali, Sarthak Bisht, Rita Singh +1
Speaker verification at large scale remains an open challenge as fixed-margin losses treat all samples equally regardless of quality. We hypothesize that mislabeled or degraded sam…
VerLM: Explaining Face Verification Using Natural Language
Syed Abdul Hannan, Hazim Bukhari, Thomas Cantalapiedra +4
Face verification systems have seen substantial advancements; however, they often lack transparency in their decision-making processes. In this paper, we introduce an innovative Vi…
DELULU: Discriminative Embedding Learning Using Latent Units for Speaker-Aware Self-Trained Speech Foundational Model
Massa Baali, Rita Singh, Bhiksha Raj
Self-supervised speech models have achieved remarkable success on content-driven tasks, yet they remain limited in capturing speaker-discriminative features critical for verificati…
SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions
Massa Baali, Sarthak Bisht, Francisco Teixeira +3
Speaker verification (SV) models are increasingly integrated into security, personalization, and access control systems, yet their robustness to many real-world challenges remains…
CoLMbo: Speaker Language Model for Descriptive Profiling
Massa Baali, Shuo Han, Syed Abdul Hannan +5
Speaker recognition systems are often limited to classification tasks and struggle to generate detailed speaker characteristics or provide context-rich descriptions. These models p…
CAARMA: Class Augmentation with Adversarial Mixup Regularization
Massa Baali, Xiang Li, Hao Chen +3
Speaker verification is a typical zero-shot learning task, where inference of unseen classes is performed by comparing embeddings of test instances to known examples. The models pe…
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.…
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…
ESPnet-Codec: Comprehensive Training and Evaluation of Neural Codecs for Audio, Music, and Speech
Jiatong Shi, Jinchuan Tian, Yihan Wu +17
Neural codecs have become crucial to recent speech and audio generation research. In addition to signal compression capabilities, discrete codecs have also been found to enhance do…
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…
Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models
Neha Sengupta, Sunil Kumar Sahu, Bokang Jia +29
We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and instruction-tuned open generative large language models (LLMs). The models are based on the GPT-…
FOOCTTS: Generating Arabic Speech with Acoustic Environment for Football Commentator
Massa Baali, Ahmed Ali
This paper presents FOOCTTS, an automatic pipeline for a football commentator that generates speech with background crowd noise. The application gets the text from the user, applie…
Arabic Dysarthric Speech Recognition Using Adversarial and Signal-Based Augmentation
Massa Baali, Ibrahim Almakky, Shady Shehata +1
Despite major advancements in Automatic Speech Recognition (ASR), the state-of-the-art ASR systems struggle to deal with impaired speech even with high-resource languages. In Arabi…
Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study
Massa Baali, Tomoki Hayashi, Hamdy Mubarak +4
Several high-resource Text to Speech (TTS) systems currently produce natural, well-established human-like speech. In contrast, low-resource languages, including Arabic, have very l…
Creating Speech-to-Speech Corpus from Dubbed Series
Massa Baali, Wassim El-Hajj, Ahmed Ali
Dubbed series are gaining a lot of popularity in recent years with strong support from major media service providers. Such popularity is fueled by studies that showed that dubbed v…