Paper Info
Title
TAMEEM V1.0: speakers and text independent Arabic automatic continuous speech recognizer.
Abstract
This research work aims to disseminate the efforts towards developing and evaluating TAMEEM V1.0, which is a state-of-the-art pure Modern Standard Arabic (MSA), automatic, continuous, speaker independent, and text independent speech recognizer using high proportion of the spoken data of the phonetically rich and balanced MSA speech corpus. The speech corpus contains speech recordings of Arabic native speakers from 11 Arab countries representing Levant, Gulf, and Africa regions of the Arabic World, which make about 45.30 h of speech data. The recordings contain about 39.28 h of 367 sentences that are considered phonetically rich and balanced, which are used for training TAMEEM V1.0 speech recognizer, and another 6.02 h of another 48 sentences that are used for testing purposes, which are mostly text independent and foreign to the training sentences. TAMEEM V1.0 speech recognizer is developed using the Carnegie Mellon University (CMU) Sphinx 3 tools in order to evaluate the speech corpus, whereby the speech engine uses three-emitting state Continuous Density Hidden Markov Model for tri-phone based acoustic models, and the language model contains uni-grams, bi-grams, and tri-grams. Using three different testing data sets, this work obtained 7.64% average Word Error Rate (WER) for speakers dependent with text independent data set. For speakers independent with text dependent data set, this work obtained 2.22% average WER, whereas 7.82% average WER is achieved for speakers independent with text independent data set.
Year
DOI
Venue
2017
10.1007/s10772-017-9403-7
I. J. Speech Technology
Keywords
Field
DocType
Modern Standard Arabic, Text corpus, Speech corpus, Phonetically rich, Phonetically balanced, Automatic continuous speech recognition
Speech corpus,Computer science,Word error rate,Text corpus,Speech recognition,Modern Standard Arabic,Natural language processing,Test data,Artificial intelligence,VoxForge,Hidden Markov model,Language model
Journal
Volume
Issue
ISSN
20
2
1572-8110
Citations 
PageRank 
References 
1
0.38
19
Authors
1
Name
Order
Citations
PageRank
Mohammad A. M. Abushariah1476.02