In detection-based, bottom-up speech recognition procedures, the segmental features like phonological feature based speech attributes act as one of the key component for the recognition model. In this study, place and manner of articulation based phonological features have been detected and they are integrated with the supra segmental parameters of speech to develop the ASR system for various under-resourced languages. For detection purpose a bank of phonological feature detector has been designed. Deep Neural Network (DNN) based attribute detector performed well to detect the phonological features. This paper also reports a comparative distribution of the (DNN) based attribute detector and the same using multi layer Perceptron (MLP). For continuous spoken speech, the Bengali CDAC speech corpus has been used. The deep neural based attribute detector achieved an average frame level accuracy of 88.56% is achieved whereas the same for MLP based detector is measured as 83.69%.