14 citations · 46 across the 26 of their papers we have counts for
8 papers · 1 filter
AraYoungVoices: A Diverse L1/L2 Corpus of Arabic Child and Adolescent Speech
Shammur Absar Chowdhury, Zien Sheikh Ali, Houssam Eddine-Othman Lachemat +1
State-of-the-art ASR systems primarily target native adult speech, leading to substantial performance gaps for children, adolescents, and L2 speakers. We introduce AraYoungVoices,…
Child ASR Adaptation with Adult Retention: An Empirical Study
Houssam Eddine-Othman Lachemat, Shammur Absar Chowdhury
Automatic Speech Recognition (ASR) systems often underperform for children and non-native speakers, while adapting adult ASR models to child speech can cause adult-speech forgettin…
Light-weight Pronunciation Assessment via Discrete Speech Token Surprisal
Syeda Faiza Ahmed Sara, Shammur Absar Chowdhury
Training automated pronunciation assessment often relies on labeled learner errors or non-native corpora that are costly to collect. We propose a lightweight framework trained only…
WASIL: In-the-Wild Arabic Spoken Interactions with LLMs
Zien Sheikh Ali, Hamdy Mubarak, Soon-Gyo Jung +3
Large Language Models (LLMs) voice assistants are commonly built as cascaded Automatic Speech recognition (ASR) to LLM systems, where recognition errors can distort user intent. Di…
Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages
Firoj Alam, Shammur Absar Chowdhury, Enamul Hoque Prince
Multimodal LLMs are evolving from vision-language to tri-modality that see, hear, and read, yet pipelines and benchmarks remain English-centric and compute-heavy. The tutorial offe…
IQRA 2026: Interspeech Challenge on Automatic Pronunciation Assessment for Modern Standard Arabic (MSA)
Yassine El Kheir, Amit Meghanani, Mostafa Shahin +5
We present the findings of the second edition of the IQRA Interspeech Challenge, a challenge on automatic Mispronunciation Detection and Diagnosis (MDD) for Modern Standard Arabic…
HARNESS: Lightweight Distilled Arabic Speech Foundation Models
Vrunda N. Sukhadia, Shammur Absar Chowdhury
Large self-supervised speech (SSL) models achieve strong downstream performance, but their size limits deployment in resource-constrained settings. We present HArnESS, an Arabic-ce…
Fanar 2.0: Arabic Generative AI Stack
FANAR TEAM, Ummar Abbas, Mohammad Shahmeer Ahmad +34
We present Fanar 2.0, the second generation of Qatar's Arabic-centric Generative AI platform. Sovereignty is a first-class design principle: every component, from data pipelines to…