## 5 Discussion  
Motivated by recent advancements in DNNs and CNNs, the present work was initiated using a CNN modeling paradigm for tonal-based speech sentences. The results section demonstrates that better and optimal results can be achieved by embedding a convolutional neural network to train and handle large speech data. The results also reveal that using a CNN over the tonal speech signals acquired significant recognition. A study on recognizing Hindi sentences using the Kaldi toolkit revealed a WER of 11.63% [32]. In contrast, the current speech recognition system designed for tonal and uncommon speech signals showed a WER of 10.56%, which is an improved word error rate compared to those of approaches developed with other techniques. This is because all training was performed  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SI. No.** & **Author(s)** & **Title of Paper/Thesis** & **Model/Technique Used** & **Accuracy Level** & **WER Word Error Rate** \\ \hline
1. & [PERSON] (2000) [16] & Tree-structured vector quantization for speech recognition & VQ & 88.5\%. & - \\ \hline
2. & [PERSON] (2003) [28] & Bayesian network structures and inference techniques for ASR system & HMM & 85\% & - \\ \hline
3. & [PERSON] (2012) [30] & Speaker Independent Isolated Speech Recognition System for Tamil Language using HMM & HMM & 88\% & - \\ \hline
4. & [PERSON] (2013) [31] & DTW-based Automatic Speech Recognition & DTW & 87.8\% & - \\ \hline
5. & [PERSON] (2019) [32] & Continuous Hindi Speech Recognition Using Kaldi ASR based on Deep Neural Network & KALDI & - & 11.63\% \\ \hline
6. & **Proposed System** & **Convolutional Neural Network and Python for developing Speech Recognition System for Recognizing Speech Signals with different tones** & **CNN \& Python** & **89.15\%** & **10.56\%** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of different techniques with proposed technique.  
Figure 7: Performance comparison of different techniques [16; 18; 30; 31; 32] with proposed technique (CNN).  
using a CNN, and various new features of CNN methodology were applied throughout the training and modeling phases.  
The current technique was implemented with different layers of CNN. In addition, to that we used cloud service during training phase. The literature review revealed using the MFCC is the best feature extraction technique. So, feature extraction was carried out using MFCCs and LibROSA (library of Python). Initially, the method was applied to input speech files from 11 speakers. The second layer of the CNN used 64-3 \(\times\) 3 filters to generate the feature set forwarded to the CNN for learning. Segmentation was a necessary step to carry out as the long input was not smooth enough to be used by the CNN. Due to the long speech signals and long sentences, manual segmentation was performed. Praat software was used to create small segments of speech and label them. As per the results generated, the overall accuracy of the present system was 89.15% using the second layer of the CNN and 64-3 \(\times\) 3 filters, while the WER was found to be 10.56%. When comparing the performance of the proposed system with similar parameters, this technique yielded an improved recognition accuracy rate.  
However, the most challenging part of this research work was dealing with tonal input speech signals alongside background music and other noises. In addition, some of the approaches are introduced for coping with Punjabi tone-oriented speech. Here, an ML-CNN had to deal with such noisy signals from 11 speakers. Still, with the chosen hyperparameters, the developed system yielded a lower WER and high accuracy in contrast to the existing approaches.