The ongoing development of audio datasets for numerous languages has spurred research activities towards designing smart speech recognition systems. A typical speech recognition system can be applied in many emerging applications, such as smartphone dialing, airline reservations, and automatic wheelchairs, among others. Urdu is a national language of Pakistan and is also widely spoken in many other South Asian countries (e.g., India, Afghanistan). Therefore, we present a comprehensive dataset of spoken Urdu digits ranging from 0 to 9. Our dataset has 25,518 sound samples that are collected from 740 participants. To test the proposed dataset, we apply different existing classification algorithms on the datasets including Support Vector Machine (SVM), Multilayer Perceptron (MLP), and flavors of the EfficientNet. These algorithms serve as a baseline. Furthermore, we propose a convolutional neural network (CNN) for audio digit classification. We conduct the experiment using these networks, and the results show that the proposed CNN is efficient and outperforms the baseline algorithms in terms of classification accuracy.

Requirements engineering requires intensive collaboration among team members. Agile methods also require constant collaboration among those involved in the project. While working on certain interdependent tasks, team members develop social and technical relationships that instigate socio-technical dependencies. The main goal of our research is to investigate socio-technical aspects that underlie requirements-driven collaboration among agile teams and their influence on project performance. In this paper we present our research approach to achieve such goal and briefly report on preliminary findings. A survey revealed that communication and awareness are the most relevant socio-technical aspects that underlie requirements-driven collaboration in agile teams. Initial findings of a case study aiming to identify requirements-driven collaboration patterns suggest that teams well aware of each other have lesser communication gaps and require lesser rework. Findings will contribute to a better understanding of the relationship between collaboration and performance in agile teams.