scholarly journals Development of a Spoken Language Identification System for South African Languages

2009 ◽  
Vol 100 (4) ◽  
pp. 97-103 ◽  
Author(s):  
M. Peche ◽  
M.H. Davel ◽  
E. Barnard
Author(s):  
Mitsuru Baba ◽  
Naoto Hoshikawa ◽  
Hirotaka Nakayama ◽  
Tomoyoshi Ito ◽  
Atsushi Shiraki

2007 ◽  
Vol 98 (4) ◽  
pp. 141-146
Author(s):  
G. Botha ◽  
V. Zimu ◽  
E. Barnard

Literator ◽  
2008 ◽  
Vol 29 (1) ◽  
pp. 185-204 ◽  
Author(s):  
P.N. Zulu ◽  
G. Botha ◽  
E. Barnard

Two methods for objectively measuring similarities and dissimilarities between the eleven official languages of South Africa are described. The first concerns the use of n-grams. The confusions between different languages in a text-based language identification system can be used to derive information on the relationships between the languages. Our classifier calculates n-gram statistics from text documents and then uses these statistics as features in classification. We show that the classification results of a validation test can be used as a similarity measure of the relationship between languages. Using the similarity measures, we were able to represent the relationships graphically. We also apply the Levenshtein distance measure to the orthographic word transcriptions from the eleven South African languages under investigation. Hierarchical clustering of the distances between the different languages shows the relationships between the languages in terms of regional groupings and closeness. Both multidimensional scaling and dendrogram analysis reveal results similar to well-known language groupings, and also suggest a finer level of detail on these relationships.


Language is the ability to communicate with any person. Approximate number of spoken languages are 6500 in the world. Different regions in a world have different languages spoken. Spoken language recognition is the process to identify the language spoken in a speech sample. Most of the spoken language identification is done on languages other than Indian. There are many applications to recognize a speech like spoken language translation in which the fundamental step is to recognize the language of the speaker. This system is specifically made to identify two Indian languages. The speech data of various news channels is used that is available online. The Mel Frequency Cepstral Coefficients (MFCC) feature is used to collect features from the speech sample because it provides a particular identity to the different classes of audio. The identification is done by using MFCC feature in the Deep Neural Network. The objective of this work is to improve the accuracy of the classification model. It is done by making changes in several layers of the Deep Neural Network.


Sign in / Sign up

Export Citation Format

Share Document