The performance of automatic speech recognition (ASR) has improved tremendously due to the application of deep neural networks (DNNs). Despite this progress, building a new ASR system remains a challenging task, requiring various resources, multiple training stages and significant expertise. In this talk, I will present an approach that drastically simplifies building acoustic models for the existing weighted finite state transducer (WFST) based decoding approach, and lends itself to end-to-end speech recognition, allowing optimization for arbitrary criteria. Acoustic modeling now involves learning a single recurrent neural network (RNN), which predicts context-independent targets (e.g., syllables, phonemes or characters). The connectionist temporal classification (CTC) objective function marginalizes over all possible alignments between speech frames and label sequences, removing the need for a separate alignment of the training data. We present a generalized decoding approach based on weighted finite-state transducers (WFSTs), which enables the efficient incorporation of lexicons and language models into CTC decoding. Experiments show that this approach achieves state-of-the-art word error rates, while drastically reducing complexity and speeding up decoding when compared to standard hybrid DNN systems.