Analysis of Automatic Speech Recognition Systems for Indo-Aryan Languages: Punjabi a Case Study
Wiqas Ghai1, Navdeep Singh2
1Wiqas Ghai, Assistant Professor, Department of Computer Science, Khalsa College (ASR) of Technology & Business Studies.
2Mr. Navdeep Singh, Senior Lecturer, Post Graduate Department of Computer Science, Mata Gujri College, Fatehgarh Sahib.
Manuscript received on February 15, 2012. | Revised Manuscript received on February 20, 2012. | Manuscript published on March 05, 2012. | PP: 379-385 | Volume-2 Issue-1, March 2012. | Retrieval Number: A0456022112/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Punjabi, Hindi, Marathi, Gujarati, Sindhi, Bengali, Nepali, Sinhala, Oriya, Assamese, Urdu are prominent members of the family of Indo-Aryan languages. These languages are mainly spoken in India, Pakistan, Bangladesh, Nepal, Sri Lanka and Maldive Islands. All these languages contain huge diversity of phonetic content. In the last two decades, few researchers have worked for the development of Automatic Speech Recognition Systems for most of these languages in such a way that development of this technology can reach at par with the research work which has been done and is being done for the different languages in the rest of the world. Punjabi is the 10th most widely spoken language in the world for which no considerable work has been done in this area of automatic speech recognition. Being a member of Indo-Aryan languages family and a language rich in literature, Punjabi language deserves attention in this highly growing field of Automatic speech recognition. In this paper, the efforts made by various researchers to develop automatic speech recognition systems for most of the Indo-Aryan languages, have been analysed and then their applicability to Punjabi language has been discussed so that a concrete work can be initiated for Punjabi language.
Keywords: Maximum likelihood linear regression, Learning vector quantization, Multi layer perceptron, Cooperative heterogeneous artificial neural network.