## 5 Conclusions  
This paper presented a novel, non-invasive pathological voice detection system considering a cochlear simulation model. Two models have been considered in this work. One model uses a bank of bandpass filters, and the other uses gammatone filters. It has been \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Research** & **Phonemes** & **Pathological** & **Features** & **Tools** & _Accuracy/_ **F1 Score** \\ \hline \multirow{3}{*}{[PERSON] [27]} & \multirow{3}{*}{Vowels} & Neoplasm, & \multirow{3}{*}{Mel Spectrogram} & \multirow{3}{*}{Dense-net} & \multirow{3}{*}{_Accuracy:_ 71\%} \\  & & phono-trauma, & & & \\  & vocal palsy & & & & \\ \hline \multirow{3}{*}{[PERSON] [31]} & \multirow{3}{*}{Tamil phrases} & Multiple & Signal energy, pitch, & \multirow{3}{*}{_Accuracy:_ 91\% (RBFNN)} \\  & & voice & & mean square residual & \\  & & disorders & & signal, reflection & RBFNN \\ \cline{3-5}  & & & coefficients, jitter & & \\ \cline{3-5}  & & & and shimmer & & \\ \hline \multirow{3}{*}{[PERSON] [33]} & \multirow{3}{*}{Japanese Vowel} & Roughness, & Higher-Order & \multirow{3}{*}{FFNN, \
\begin{tabular}{} \end{tabular} } \\  & & breathiness, & Local & & \\  & & asthenia, & Autocorrelation & AR-HMM & for \\  & & and strain & (HLAC) & & speaker-based \\ \hline \multirow{3}{*}{[PERSON] [21]} & \multirow{3}{*}{Vowels} & [PERSON]’s edema, & \multirow{3}{*}{Spectrogram} & \multirow{3}{*}{CNN,
\begin{tabular}{} \end{tabular} } \\  & & laryngitis, & & \\  & & leukoplakia, & & \\ \cline{1-1}  & & recurrent laryngeal, & & CDBN & \\ \cline{1-1}  & & nerve paralysis, & & \\ \cline{1-1}  & & vocal fold carcinoma, & & & \\ \cline{1-1} \cline{2-5}  & & & **Cochlear Simulation** & **Implant** & **77.6\%,** \\ \cline{1-1}  & & & **Model-1,** & **processing** & **78.7\%** \\ \cline{1-1}  & & & **Cochlear Simulation** & **system and** & _Accuracy:_ 77.9\%,_ \\ \cline{1-1}  & & & **CNN** & **77.5\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The performance comparisons of some published pathological voice detection systems. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **System Model** & \\ \hline
**Measures** & **Bandpass Filters** & **Gammatone Filters** \\ \hline _Accuracy_ & **77.90** & 77.50 \\ _Precision_ & **78.60** & 74.60 \\ _Recall/Sensitivity_ & 76.70 & **83.30** \\ _F1 score_ & 77.60 & **78.70** \\ _NPV_ & 77.20 & **81.10** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison of two system models with gammatone and bandpass filters. shown that the gammatone filter is more suitable for voice pathology identification through the signal processing steps involved in the cochlear implants. It has also been demonstrated that the gammatone filters with \(f_{0}/b=9\) are the optimum choice for this purpose. The speech samples have been processed using these two models and the processed signals were applied to the input of a CNN, which acted as a binary classifier to detect pathological voices. It is a challenging issue to consider suitable features extracted from the speech samples. In general, no single feature or feature vector is well-accepted to provide the best accuracy. This novel technique eliminates acoustic feature extraction from the speech samples before applying the classification algorithm. The simulation results presented in this paper have shown that the proposed system achieved almost equal accuracy by using the two proposed models. However, the higher _F1 score_ for the model with gammatone filters illustrates its better applicability for pathological voice identification through the cochlear implant simulation model. This work focused only on discriminating pathological voices from normal voices using a signal processing approach adopted in the simulated cochlear implant processing system. However, achieving better performance in terms of accuracy and determining the progression level of voice pathology should be considered as the next challenges.