Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

the digital spectra is broken up in to four parts corresponding to “dinubabur”,
“desher”, “nam”, and “phalakata”. The analysis of the complete sentence is
done to acquire some useful speech parameters, which help in the
identification of the speaker with the Speech Processing toolbox in MATLAB
on a PENTIUM based system. The various features acquired by the
MATLAB are:
1. Formant frequencies[18] (fundamental and the harmonics),
2. Peak power spectrum density (at different frequency ranges),
3. Average value of the spectral density of the word “nam” which is in the
CVC context (Consonant followed by a Vowel followed by the
Consonant) at different range of frequency, and
4. The pitch, which is estimated by counting the number of pulses per second
by taking the digitized data of sampling frequency 10240 Hz.
A sample of estimation of the formant frequencies, from the digitized speech
signal of a person, uttering the above sentence, is presented in fig. 23.6.
Frequency
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0 200 400 600
Time
800 1000 1200
The dark horizontal shades correspond to the formant frequencies of
specified duration. First 8- formant frequencies are visible in the figure.
Fig. 23.6: The speech spectrogram, showing the formant frequencies.