Paper Info
Title
Development and Analysis of Speech Recognition Systems for Assamese Language Using HTK.
Abstract
Language analysis is very important for the native speaker to connect with the digital world. Assamese is a relatively unexplored language. In this report, we analyze different aspects of speech-to-text processing, starting from building a speech corpus, defining syllable rules, and finally developing a speech search engine of Assamese. We have collected about 20 hours of speech in three (viz., read, extempore, and conversation) modes and transcribed it. We also discuss some issues and challenges faced during development of the corpus. We have developed an automatic syllabification model with 11 rules for the Assamese language and found an accuracy of more than 95% in our result. We found 12 different syllable patterns where 5 are found most frequent. The maximum length of a syllable found is four letters. With the help of Hidden Markov Model Toolkit (HTK) 3.5, we used deep learning based neural network for our speech recognition model, where we obtained 78.05% accuracy for automatic transcription of Assamese speech.
Year
DOI
Venue
2017
10.1145/3137055
ACM Trans. Asian & Low-Resource Lang. Inf. Process.
Keywords
Field
DocType
Assamese, HTK, Speech search engine, automatic transcription, speech corpus, syllabification
Speech corpus,Assamese,Computer science,Syllabification,Speech recognition,Natural language processing,Syllable,Artificial intelligence,Deep learning,VoxForge,Hidden Markov model,First language
Journal
Volume
Issue
ISSN
17
1
2375-4699
Citations 
PageRank 
References 
2
0.37
17
Authors
3
Name
Order
Citations
PageRank
Himangshu Sarma162.51
Navanath Saharia2276.09
Utpal Sharma3578.50