0% Complete
Home
/
14th International Conference on Computer and Knowledge Engineering
AvashoG2P: A multi-module G2P Converter for Persian
Authors :
Ali Moghadaszadeh
1
Fatemeh Pasban
2
Mohsen Mahmoudzadeh
3
Maryam Vatanparast
4
Amirmohammad Salehoof
5
1- Part AI Research Center
2- Part AI Research Center
3- Ferdowsi University of Mashhad
4- Part AI Research Center
5- Part AI Research Center
Keywords :
TTS،G2P
Abstract :
The conversion of graphemes to phonemes (G2P) is a fundamental task in text-to-speech (TTS) and automatic speech recognition (ASR) systems. Over the years, G2P systems have evolved from rule-based and statistical methods to advanced neural network-based approaches. Despite these advancements, G2P conversion for Persian remains challenging due to the complex relationship between spelling and pronunciation and the scarcity of high-quality datasets. This paper introduces the AvashoG2P, a multi-module novel solution for Persian G2P conversion. The AvashoG2P system leverages a sequence-to-sequence (seq2seq) model with a GRU-based recurrent unit and an attention mechanism. This model is trained on both diacritized and non-diacritized words, enhancing its understanding of phonemes and their relationships. The system achieves a Word Error Rate (WER) of 15\% and a Phoneme Error Rate (PER) of 5\%, demonstrating its effectiveness. One of the critical components of AvashoG2P is its homograph disambiguation module, which utilizes a single model for all homographs, addressing a significant challenge in Persian text processing. Our method leverages a classification approach for homograph disambiguation, which assigns a phoneme label to the entire input window. Our system achieves high accuracy while optimizing for latency and memory consumption. We achieve significant improvements in accuracy and F1 scores using transformer-based models and machine learning classifiers. Our results highlight the superior performance of the XLMRoberta model among transformer models, with an F1 Weighted score of 94.7, and the SVC model among machine learning classifiers, with an F1 Weighted score of 89.96. Additionally, we present the AvashoG2P-Benchmark, a comprehensive test dataset designed to facilitate future research and benchmarking in Persian G2P tasks (available at: https://huggingface.co/datasets/PartAI/AvashoG2P-Benchmark).
Papers List
List of archived papers
Efficient Sub-Carrier Relationship Extraction for Human Activity Recognition via EEGNet in Wireless Sensing
Siavash Zaravashan - Sadegh ArefiZadeh - Sajjad Torabi
Leveraging Self-Supervised Models for Automatic Whispered Speech Recognition
Aref Farhadipour - Homa Asadi - Volker Dellwo
Identification of Botnets and Nodes Attacking Smart Cities by Majority Voting Mechanism and Feature Selection
Maliheh Araghchi - Nazbanoo Farzaneh
GroupRec: Group Recommendation by Numerical Characteristics of Groups in Telegram
Davod Karimpour - Mohammad Ali Zare Chahooki - Ali Hashemi
An overview of Business Intelligence research in healthcare organizations using a topic modeling approach
Mohammad Mehraeen - Laya Mahmoudi - Mohammad Hossein Sharifi
Crack Segmentation in Civil Structure Images Using a Deep Learning Based Multi-Classifier System
Mohammadreza Asadi - Seyedeh Sogand Hashemi - Mohammad Taghi Sadeghi
ExaAEC: A New Multi-label Emotion Classification Corpus in Arabic Tweets
Saeed Sarbazi-Azad - Ahmad Akbari - Mohsen Khazeni
Blind image quality assessment based on Multi-resolution Local Structures
Seyed Majid Khorashadizadeh - Mehdi Sadeghi Bakhi - Fatemeh Seifishahpar - AliMohammad Latif
A supervised approach using transformer networks for the detection of turning-related anomalies in urban intersections
Mohammad Mahdi HajiAbadi - Manoochehr Nahvi
Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation Technique
Aref Farhadipour - Pouya Taghipour
more
Samin Hamayesh - Version 41.3.1