An Efficient Mispronunciation Detection System
Using Discriminative Acoustic Phonetic Features
for Arabic Consonants
Muazzam Maqsood1, Adnan Habib2,
and Tabassam Nawaz1
1Department of Software
Engineering, University of Engineering and Technology Taxila, Pakistan
2Department of
Computer Science, University of Engineering and Technology Taxila, Pakistan
Abstract: Mispronunciation
detection is an important component of Computer-Assisted Language Learning
(CALL) systems. It helps students to learn new languages and focus on their
individual pronunciation problems. In this paper, a novel discriminative Acoustic
Phonetic Feature (APF) based technique is proposed to detect mispronunciations
using artificial neural network classifier. By using domain knowledge, Arabic
consonants are categorized into two
groups based on their acoustic similarities. The first group consists of
consonants having similar ending sounds and the second group consists of consonants
with completely different sounds. In our proposed technique, the discriminative
acoustic features are required for classifier training. To extract these
features, discriminative parts of the Arabic consonants are identified. As a
test case, a dataset is collected from
native/non-native, male/female and children of different ages. This dataset
comprises of 5600 isolated Arabic consonants. The average accuracy of the system,
when tested with simple acoustic features are found to be 73.57%.While the use
of discriminative acoustic features has improved the average accuracy to
82.27%. Some consonant pairs that are acoustically very similar, produced poor results and termed as Bad Phonemes. A
subjective analysis has also been carried out to verify the effectiveness of
the proposed system.
Keywords: Computer assisted
language learning systems, mispronunciation detection, acoustic-phonetic features,
artificial neural network, confidence measures.