## 1 Introduction  
Deep Learning has been successful in multiple domains including image classification [1, 2, 3], text classification [4, 5], speech recognition [6, 7, 8], and many more [9]. In speech recognition, there has been a tremendous amount of work done ranging from speech recognition to speech mimicry for multiple languages. However, in all languages, data have been a key challenge for any deep learning task, as data are difficult to find, and data collection is a time-consuming and tedious task to do. Without data, research and development for any language is not possible. To continue the robust and reliable development of speech recognition, it is necessary to have publicly available data so researchers can continue to develop speech-related systems. Similarly, for Urdu audio digit classification, there is no dataset available publicly. Inspired by the need for audio digit recognition, we release the Urdu audio digit dataset. Typically, a framework for automatic speech recognition [10] in Urdu can assist voice recognition in other languages that are similar [11].  
A few works [12, 13] considered speech processing schemes in the Urdu language. The field of computer science associated with the interactions with computers and natural languages is known as Natural Language Processing (NLP). This work is related to Automatic Speech Recognition (ASR), which is the advanced research domain of the NLP [12]. Automatic Speech Recognition (ASR) is the transformation of an acoustical waveform into the text equivalent of the information conveyed by the spoken word. The main objective of ASR research is to enable the computers that can recognize continuous speech with limitless vocabulary. This aim remains unfulfilled, as yet, although substantial progress has been achieved. Artificial neural networks (ANNs) were created to mimic the human brain. ANNs perform excellent classification and are used in a variety of pattern classification applications. ANNs are a suitable selection for Automatic Speech Recognition (ASR) systems because of their exceptional classification abilities. Most of the modern speech recognition systems that have been created use ANNs for speech signals classification [13]. Despite the importance of ASR, the research for Urdu ASR is a quite new domain because of two reasons: (1) lack of Urdu language studies research and (2) lack of a balanced speech dataset.  
Currently, some work studied ASR for Urdu [13; 14; 15; 16; 17; 18; 19; 20]. Additionally, numerous works [12; 17; 21; 22; 23] have been done on spoken digit recognition for Urdu. The above-mentioned works have shown significant results. However, the considered datasets were not comprehensive; thus, trained machine learning models can be further improved by using large datasets. To address this issue, we propose a comprehensive Audio Urdu Digits Dataset (AUDD). The main contributions of our work are as follows.  
* We present an AUDD for Urdu digits that comprises 0-9. We have collected these voice samples from 740 people. These people were of different age groups ranging from 5 to 89 years.
* We perform extensive experiments for different networks to provide classification accuracy that will serve as baseline accuracy.
* We propose a convolutional neural network (CNN) for classification that shows impressive performance being a simple CNN compared to complex flavors of efficientNet.  
The remaining work is organized as follows: Section 2 gives an overview of relevant work that has been done previously, Section 3 describes data collection in detail, Section 5 describes the details of the experiment and baseline results, and finally Section 6 concludes the overall contribution.