Standard automatic speech recognition (ASR) systems follow a divide and conquer approach to convert speech into text. Alternately, the end goal is achieved by a combination of sub-tasks, namely, feature extraction, acoustic modeling and sequence decoding, which are optimized in an independent manner. More recently, in the machine learning community deep learning approaches have emerged which allow training of systems in an end-to-end manner. Such approaches have found success in the area of natural language processing and computer vision community, and have consequently peaked interest in the speech community. The present thesis builds on these recent advances to investigate approaches to develop speech recognition systems in end-to-end manner. In that respect, the thesis follows two main axes of research. The first axis of research focuses on joint learning of features and classifiers for acoustic modeling. The second axis of research focuses on joint modeling of the acoustic model and the decoder. Along the first axis of research, in the framework of hybrid hidden Markov model/artificial neural networks (HMM/ANN) based ASR, we develop a convolution neural networks (CNNs) based acoustic modeling approach that takes raw speech signal as input and estimates phone class conditional probabilities. Specifically, the CNN has several convolution layers (feature stage) followed by multilayer perceptron (classifier stage), which are jointly optimized during the training. Through ASR studies on multiple languages and extensive analysis of the approach, we show that the proposed approach, with minimal prior knowledge, is able to learn automatically the relevant features from the raw speech signal. This approach yields systems that have less number of parameters and achieves better performance, when compared to the conventional approach of cepstral feature extraction followed by classifier training. As the features are automatically learned from the signal, a natural question that arises is: are such systems robust to noise? Towards that we propose a robust CNN approach referred to as normalized CNN approach, which yields systems that are as robust as or better than the conventional ASR systems using cepstral features (with feature level normalizations). The second axis of research focuses on end-to-end sequence-to-sequence conversion. We first propose an end-to-end phoneme recognition system. In this system the relevant features, classifier and the decoder (based on conditional random fields) are jointly modeled during training. We demonstrate the viability of the approach on TIMIT phoneme recognition task. Building on top of that, we investigate a ``weakly supervised'' training that alleviates the necessity for frame level alignments. Finally, we extend the weakly supervised approach to propose a novel keyword spotting technique. In this technique, a CNN first process the input observation sequence to output word level scores, which are subsequently aggregated to detect or spot words. We demonstrate the potential of the approach through a comparative study on LibriSpeech with the standard approach of keyword word spotting based on lattice indexing using ASR system.