Analysis of Formant Frequency F1, F2 and F3 in Assamese Vowel Phonemes using LPC Model Dr. Bhargab Medhi Department of Applied Science Gauhati University Guwahati, Assam, India Abstract—Formant frequency plays an important role in speech as well as speaker recognition. Formants are the spectral peaks of a sound wave which means the specific resonance frequencies of vocal tract which have maximum energy concentration during the vowel utterances. In a speech spectrum, there may be any number of formants, but for speech the most informative are the first three formants referred to as F1, F2, and F3. In this paper, the paths of these three formants are analyzed in Assamese vowel phonemes. LPC model is used to identify the formant frequencies. Keywords— Formant, LPC, Vowel Phoneme, Spectrum, Filter. I. INTRODUCTION Assamese (IPA: ɔxɔmija) is a native language of Assam which is a major language in the north-eastern India. Its root is Indo-European family of languages. Assamese scripts is derived from the Devanagari scripts consisting thirty nine consonants and eleven vowel symbols which are arranged in a well structured scientific manner[6]. Though there are eleven vowel symbols in Assamese script, but the number of vowel phonemes is only eight. A phoneme is nothing but a single unit of sound that has a meaning in that language. The vowel is the largest phoneme group as the source for vowel is quasi-periodic puffs of airflow through the vocal folds vibrating at a certain fundamental frequency. Each vowel phoneme corresponds to a different vocal tract configuration. Different studies say that the first three formant frequencies measured in the steady- state part of a vowel play an important role in its characterization. The formants of the same vowel uttered by different speakers, in different contexts, at different speaking rates and with different stress patterns, show a lot of variability [5]. From the last few decades, a number of well approaches have been developed for analysis and synthesis of speech signal with a view for speaker/speech recognition. Among those approaches formant estimation is considered as one of the basic models for speech recognition and research. The formant model is used for the determination of formant frequency of Assamese vowels based on the model proposed by L. Welling. The basic idea behind the LPC model is that a given speech sample at time can be approximated as a linear combination of the past speech samples [1,5]. The formant frequencies are computed by taking peak the LPC spectrum. In the first phase during this work, a small database for eight Assamese vowel phonemes is created recording each phoneme 10 times, uttered by ten numbers of Assamese native speakers of equal number of male and female. The recording is done in an acoustic studio in a noise free environment where the utterances are kept normal, stress free and intonation flat. The written symbols of Assamese vowel scripts and their corresponding vowel phonemes are presented in the following TABLE I. TABLE I: Assamese vowel phonemes and their positions II. LPC MODEL AND FORMANT FREQUENCY Speech signal is formed by the convolution of excitation source and time varying vocal tract components.LPC is a method of separating out the effects of source and filter from a speech signal. The cepstral analysis is the deconvolution of speech into source and system components by traversing through frequency domain. LPC is a tool used mostly in audio signal processing and speech processing for representing the spectral envelop of a digital signal of speech in compressed form, using the information of a linear predictive model [5]. In LP analysis of speech, an all pole model is assumed for the system producing speech signal s(n).The predicted sample can be represented as by (1). 1 () ( ) () p i i sn as n i Gu n      --- (1) Where, ai (i=1, 2, 3, . . ., p) are the co-efficients assumed to be constant over the speech analysis frame. The u(n) is the normalized excitation and G is the gain of excitation. If ˆ() sn is the estimate value of () sn calculated from the linear combination of past p-samples, then we can get the (2). International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 http://www.ijert.org IJERTV6IS050422 (This work is licensed under a Creative Commons Attribution 4.0 International License.) Published by : www.ijert.org Vol. 6 Issue 05, May - 2017 616