Probabilistic asr feature extraction applying context-sensitive connectionist temporal classification networks

This paper proposes a novel automatic speech recognition (ASR) front-end that unites the principles of bidirectional Long Short-Term Memory (BLSTM), Connectionist Temporal Classification (CTC), and Bottleneck (BN) feature generation. BLSTM networks are known to produce better probabilistic ASR features than conventional multilayer perceptrons since they are able to exploit a self-learned amount of temporal context for phoneme estimation. Combining BLSTM networks with a CTC output layer implies the advantage that the network can be trained on unsegmented data so that the quality of phoneme prediction does not rely on potentially error-prone forced alignment segmentations of the training set. In challenging ASR scenarios involving highly spontaneous, disfluent, and noisy speech, our BN-CTC front-end leads to remarkable word accuracy improvements and prevails over a series of previously introduced BLSTM-based ASR systems.