6 papers
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…
Position-invariant Fine-tuning of Speech Enhancement Models with Self-supervised Speech Representations
Amit Meghanani, Thomas Hain
Integrating front-end speech enhancement (SE) models with self-supervised learning (SSL)-based speech models is effective for downstream tasks in noisy conditions. SE models are co…
Towards a Unified Benchmark for Arabic Pronunciation Assessment: Quranic Recitation as Case Study
Yassine El Kheir, Omnia Ibrahim, Amit Meghanani +12
We present a unified benchmark for mispronunciation detection in Modern Standard Arabic (MSA) using Qur'anic recitation as a case study. Our approach lays the groundwork for advanc…
LASER: Learning by Aligning Self-supervised Representations of Speech for Improving Content-related Tasks
Amit Meghanani, Thomas Hain
Self-supervised learning (SSL)-based speech models are extensively used for full-stack speech processing. However, it has been observed that improving SSL-based speech representati…
Improving Acoustic Word Embeddings through Correspondence Training of Self-supervised Speech Representations
Amit Meghanani, Thomas Hain
Acoustic word embeddings (AWEs) are vector representations of spoken words. An effective method for obtaining AWEs is the Correspondence Auto-Encoder (CAE). In the past, the CAE me…
SCORE: Self-supervised Correspondence Fine-tuning for Improved Content Representations
Amit Meghanani, Thomas Hain
There is a growing interest in cost-effective self-supervised fine-tuning (SSFT) of self-supervised learning (SSL)-based speech models to obtain task-specific representations. Thes…