## 1 Introduction  
The performance of the speech recognition process has been enhanced using deep neural networks (DNNs). DNNs, as a subfield of machine learning (ML), comprise a hierarchical computation framework, where multiple layers of efficient learning algorithms are grouped to execute specific tasks. DNNs approximate the process using various non-linear activation functions to obtain the conceptual view. They also serve the learning process using generated feature sets from original data (speech signals). Due to the significant progress made in DNNs in the last decade, this approach has been used in diverse applications ranging from online data, filtering, dictation, web searches, speech recognition, speaker identification, image processing, and many machine intelligence-related problems. The most crucial phase of DNN was discovered in the 1980s, namely back propagation algorithm (BPA). This algorithm incorporated the gradient descent method to accelerate the training process at a rapid rate [1]. Convolutional neural networks (CNNs) are successful variants of DNNs and valuable models for working with speech recognition systems. CNNs are moderately successful models for developing the speech recognition system, but this efficient architecture design is quite complicated. Their design requires prior and expert knowledge for performing the recognition process [2]. The natural visual perception paradigm of living creatures inspired this learning architecture [3]. The name was derived from linear mathematical operations between matrixes known as convolutions. Many studies have validated that CNNs provide better results for classification tasks. However, the standard CNN model may be impractical due to its high computational cost and slow learning procedure for large datasets. This paper presents a CNN-based novel approach for uncommon speech signal recognition. While exploring the literature, it was observed that work related to Punjabi language speech recognition was performed using the Kaldi toolkit. The results were extended to the uncommon speech signals of Gurbani recitation, along with background music, which also reveals this study's novelty and additional contribution to the field of speech recognition [4; 5; 6; 7]. The main objective of the work was achieved by adopting and implementing the CNN framework. Mainly, two properties of the CNN, including the local frequency region, support achieving optimal performance, making the CNN more robust. In addition, the sparse local connections of the convolutional layer that need very few parameters to extract lower-level features of the input speech signal avoid overfitting [8; 9; 10]. Input speech signals were examined with spectro-temporal filters, and the CNN learned about the lower-level features. No inbuilt data were used for this work; a database with different input speech signals was designed to achieve the main aim of the work. A CNN can work better than a DNN as far as noisy channels are concerned, and higher tolerance is expected with a CNN. In addition, a CNN is better for distorted speech signals as there is a difference between the speaker's lips and the microphone as well as additive noise in speech signals [8]. Figure 1 demonstrates the novel architecture of a CNN with its three layers--convolutional layer, pooling layer, and fully connected layer. Various convolution kernels obtain the feature map from the previous layers for each convolutional layer. To obtain the feature map, the input signal is combined with its trained kernel [11; 12; 13]. For many years, various technologies and modeling approaches have been employed for training and recognizing common and uncommon speech signals, yet systems do not work well for tonal speech sentences. Finally, recent advancements in machine learning  
Figure 1: Architecture of a typical CNN. (ML) have motivated us to train the present speech recognition system with extensive data and tonal speech sentences.