Work

A Corpus and Phonetic Dictionary for Tunisian Arabic Speech Recognition

Year: 2014

Type: conference-paper

Abstract: In this paper we describe an effort to create a corpus and phonetic dictionary for Tunisian Arabic Automatic Speech Recognition (ASR).The corpus, named TARIC (Tunisian Arabic Railway Interaction Corpus) has a collection of audio recordings and transcriptions from dialogues in the Tunisian Railway Transport Network.The phonetic (or pronunciation) dictionary is an important ASR component that serves as an intermediary between acoustic models and language models in ASR systems.The method proposed in this paper, to automatically generate a phonetic dictionary, is rule based.For that reason, we define a set of pronunciation rules and a lexicon of exceptions.To determine the performance of our phonetic rules, we chose to evaluate our pronunciation dictionary on two types of corpora.The word error rate of word grapheme-to-phoneme mapping is around 9%.

Source: Language Resources and Evaluation Conference (LREC)

DOI: 10.63317/2x7os8jjo9dx

Authors: Abir Masmoudi, Mariem Ellouze Khmekhem, Yannick Estève, Lamia Hadrich Belguith, Nizar Habash

Institutions: University of Sfax, Laboratoire d’Informatique de l’Université du Mans, Columbia University

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W2250960632

FWCI: 2.945

Cites: 12

Cited by: 34

Related to: 10