International Journal of Scientific & Engineering Research Volume 4, Issue 2, February-2013 1 ISSN 2229-5518 IJSER © 2013 http://www.ijser.org Spectral Analysis of Pathological & Normal Speech Signal Prof. Syed Mohammad Ali, Dr. Pradeep Tulshiram Karule Abstract— Due to nature of jobs, unhealthy social habits people are subjected to risk of voice problem [9]. If there is some neurological disorder then also there is problem of voice disorder. Therefore, voice signal can be a useful tool to diagnose them. The awkwardness of analog equipments has simulated development of digital computer techniques for processing and analysis of pathological speech signal in patients care system. In this paper, normal & pathological speech signals are taken & a system is designed to differentiate normal from abnormal signals. These signals are first preprocessed. Preprocessing techniques involves passing signal through high pass filter, moving average filter (ma), framing & windowing .The windowed signal is given for spectral analysis. In spectral analysis, various methods like logarithmic spectrum, cepstrum, auto correlation of speech signal, spectrogram are applied to differentiate normal & abnormal speech signals. It has been seen that with the above method one can clearly differentiate these signals. Index Terms— Pathological speech signals; preprocessing; spectral analysis. ——————————  —————————— 1 INTRODUCTION peech disorder detection has received great momentum in the last decade. Digital signal processing has become an important tool for voice disorder detection [3]. Patho- logical voice signal & Normal signals are taken. The patholog- ical speech signals are taken from Govt. Medical College & Hospital, Nagpur & Dr. Naresh Agrawal Hospital, Nagpur. The signals are recorded keeping mic two inch away from the mouth using Voice recorder of Window XP. The sampling frequency is chosen to 11025 samples/ sec, 8 bit stereo 21 kb/ sec. The patients are told to pronounce vowel ‘a’, vowel consonant ‘ah’ & word ‘Hello’. Physicians often use invasive techniques like endoscopy to diagnose symptoms of vocal fold disorders however, it is possible to diagnose disease using certain feature of speech signal [3]. The speech signal is noisy & noise needs to be removed. So signal is pre-processed by passing it through preprocessing system. Speech signal is sinusoidal signal having different frequency, different amplitude & different phase. It is given by the expression given below [6]. i i N 1 i i A (t)sin[2 πF (t)t θ (t)]    (1) Where, Ai(t), F i(t) & θi(t) are the sets of amplitudes, frequen- cies & phases respectively, of the sinusoids & speech production requires close cooperation of numerous organs which from the phonetic point of view may be divided into organ. 1. Lungs, Bronchi, Tracheas (producing expiration air steam necessary for phonation) 2. Larynx (amplifying the initial tone) 3. Root of the tongue, throat, nasal cavity, oral cavity (forming tone quality & speech sound) [7]. The use of non invasive techniques to evaluate the larynx and vocal tract helps the speech specialists to perform accurate diagnose [10]. Speech signal in non intrusive in nature & it has potential for providing quantitative data with reasonable analy- sis time. So study of speech signal of pathological voice has be- come an important topic for research as it reduces work load in diagnoses of pathological voices [8]. Fig. 1 (a). A speech signal Fig. 1 (b). A processed speech signal Figure 1 shown above is the speech signal of a neurological disorder patient. The algorithm shown figure 2 below shows the flow of control. Here in this paper we have taken speech samples of neurological disorder and normal persons the speech samples are passed through moving average filter and high pass Filter. The filtered output is framed and then each frame is passed through window. The output signal which is framed and windowed is used for spectral analysis. In spectral analysis logarithmic spectrum of framed window signal is found. Then logarithmic spectrum is used to get cepstrum. Framed signal is also used for finding autocorrelation of speech signal. S