Papers by Hamid R. Rabiee

4 papers
ManaTTS Persian: a recipe for creating TTS datasets for lower resource languages (2025.naacl-long)

Copied to clipboard

Challenge: a new text-to-speech system is needed for visual impairments and the visually impaired . a text-based system is not available for all users, and is therefore limited to a limited audience.
Approach: They propose to use ManaTTS, the most extensive publicly accessible Persian corpus . they use a fully transparent, MIT-licensed pipeline to collect transcribed speech datasets .
Outcome: The proposed framework is the most extensive publicly accessible single-speaker Persian corpus . it includes tools for sentence tokenization, bounded audio segmentation, and forced alignment method .
Fast, Not Fancy: Rethinking G2P with Rich Data and Statistical Models (2025.findings-emnlp)

Copied to clipboard

Challenge: specific disambiguation strategies introduce additional latency, making them unsuitable for real-time applications.
Approach: They propose a semi-automated pipeline for constructing homograph-focused datasets . they introduce a HomoRich dataset and advocate for a paradigm shift .
Outcome: The proposed pipeline improves a state-of-the-art deep learning-based G2P system for Persian.
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems (N19-1)

Copied to clipboard

Challenge: Experimental results show that extreme multi-label learning improves label prediction quality by 3% to 5% in three of the 5 tasks and is competitive in the others.
Approach: They propose a submodular maximization framework with linear cost to find informative labels which are most relevant to other labels yet least redundant with each other.
Outcome: The proposed model improves label prediction quality by 3% to 5% in three of the 5 tasks and is competitive in the others.
Beyond Unified Models: A Service-Oriented Approach to Low Latency, Context Aware Phonemization for Real Time TTS (2026.eacl-industry)

Copied to clipboard

Challenge: Lightweight, real-time text-to-speech systems often rely on lightweight phonemizers . a new framework aims to bridge the trade-off between phonemization quality and inference speed .
Approach: They propose lightweight strategies for context-aware phonemization and a service-oriented TTS architecture that executes these modules as independent services.
Outcome: The proposed system improves pronunciation soundness and linguistic accuracy while maintaining real-time responsiveness.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations