Title :
Recurrent Neural Network based approach to recognize assamese vowels using experimentally derived acoustic-phonetic features
Author :
Sharma, Mukesh ; Sarma, M. ; Sarma, Kandarpa Kumar
Author_Institution :
Dept. of Electron. & Commun. Eng., Gauhati Univ., Guwahati, India
Abstract :
Vowels are the phonemes with greatest intensity and low frequencies. Assamese, which is considered as the lingua-franca of the entire north-east India, has eight vowel phonemes namely /i/, /e/, /ε/, /a/, /0/, /?/, /o/ and /u/. A Recurrent Neural Network (RNN) based algorithm is described in this paper for the recognition of the vowel sounds from Assamese speech. The feature vector is generated by considering the acoustic phonetic features of vowels like duration, fundamental frequency (F0) and the four formant frequencies (F1, F2, F3 and F4). From the experimental results a recognition rate of 84 % is obtained which can be considered to be satisfactory in comparison to the current phoneme recognition strategy.
Keywords :
feature extraction; natural language processing; recurrent neural nets; speech processing; speech recognition; Assamese speech; Assamese vowel recognition; North-East India; RNN; acoustic-phonetic features; feature vector; formant frequencies; fundamental frequency; lingua-franca; recurrent neural network based approach; vowel sound recognition; Acoustics; Frequency measurement; Recurrent neural networks; Speech; Speech recognition; Training; Vectors; Acoustic Phonetic Features; Formants; Fundamental Frequency; Recognition; Recurrent Neural Network (RNN); Vowels;
Conference_Titel :
Emerging Trends and Applications in Computer Science (ICETACS), 2013 1st International Conference on
Conference_Location :
Shillong
Print_ISBN :
978-1-4673-5249-9
DOI :
10.1109/ICETACS.2013.6691411
