## 3 Materials and Methods  
In this investigation, control (i.e., normal) and laryngitis voice samples were collected from the Saarbrucken Voice Database (SVD) [43]. The SVD database is a collection of speech and electroglottography (EGG) signals of more than 2000 speakers. It contains recordings of 1002 speakers (454 male and 548 female), exhibiting a wide range of voice disorders. It also includes recordings of 851 control (423 male and 428 female) samples. The age of the speakers varies from 6 to 84 years [44]. All of these samples were collected in one session with the patients and the samples contain the recordings of the following components: (a) vowels '/i/', '/a/', and '/u/' produced at normal, high, and low pitch, (b) vowels '/i/', '/a/', and '/u/' with rising and falling pitch, and (c) sentence, \"Guten Morgan, wie geht es Ihnen?\" (\"Good morning, how are you?\"). In this investigation, we chose the sentence, \"Guten Morgan, wie geht es Ihnen?\" (\"Good morning, how are you?\"). The main reason is that the sentence speech samples contain both voiced and unvoiced components. On the other hand, the vowel speech samples contain only the voiced component. Moreover, the sentence speech samples contain articulatory and other linguistic confounds that often do not exist with the vowel samples. Figure 3 shows the time domain plots for control (i.e., healthy) and laryngitis voice samples, randomly collected from the SVD database. It is observed in the figure that the laryngitis voice sample suffers from irregular distortion in both magnitude and shape compared to that of the healthy sample. In addition, the laryngitis voice samples exhibit a more extended unvoiced segment compared to the vowel samples. The basic building blocks of the proposed system are shown in Figure 4. This model was derived based on the commercially available Clarion 1.2 processor [45; 46; 47] introduced by Advanced Bionics Corporation in cooperation with the University of California and the Research Triangle Institute. Figure 4: The proposed system, comprised of pre-processing, cochlear modeling, and classifier. Figure 3: The healthy and pathological voice samples of “Guten Morgen, wie geht es Ihnen?” (“Good morning, how are you?”). As shown in Figure 4, the system model can be broadly classified into three major sub-systems. They are: (a) pre-processing, (b) cochlear modeling, and (c) classification. The pre-processing sub-system consists of three signal processing steps: down-sampling, D.C. removal, and pre-emphasis. In a Clarion processor, the acoustic signal is processed at the rate of 13,000 samples/s. The voice samples available in the SVD database have a sampling frequency of 50,000 samples/s. Hence, the voice signals were down-sampled to 13,000 samples/s using the MATLAB built-in function of _resample_. The _resample_ function utilizes a built-in anti-aliasing (lowpass) FIR filter to minimize the effects of aliasing that occur due to the down-sampling operation. Afterwards, the D.C. component of the speech signals was removed. Most of the energy in the speech signal is concentrated in the lower frequency components of its spectrum and, generally, the energy drops at a rate of 2.0 dB/kHz [48]. This rapid reduction in energy leads to a problem for further subsequent processing of the speech signals. To overcome this limitation, the high-frequency components of the speech signals were boosted by a pre-emphasis filter, which was designed based on the model presented in [49]. The magnitude response of the pre-emphasis filter is shown in Figure 5. This filter has a cut-off frequency of 2000 Hz and a roll-off of 3 dB/octave. It compensates for the rapid reduction of the energy in the low-frequency components of the audio signal. Additionally, it better optimizes the CPU consumption. It is also shown in Figure 4 that the cochlear modeling sub-system consists of a bandpass filter, rectifier, lowpass filter, and a non-linear mapper. The pre-processed speech signals were divided into eight channels by using eight filters.