Linguistics

Philosophy

Biology

Connectionism

Hidden Markov model

TIMIT

Sequence labeling

Economics

Computer science

Natural language processing

Genetics

Sequence (biology)

Task (project management)

Pattern recognition (psychology)

Management

Artificial intelligence

Artificial neural network

Word (group theory)

Recurrent neural network

Speech recognition

Many real-world sequence learning tasks require the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is transcribed into words or sub-word units. Recurrent neural networks (RNNs) are powerful sequence learners that would seem well suited to such tasks. However, because they require pre-segmented training data, and post-processing to transform their outputs into label sequences, their applicability has so far been limited. This paper presents a novel method for training RNNs to label unsegmented sequences directly, thereby solving both problems. An experiment on the TIMIT speech corpus demonstrates its advantages over both a baseline HMM and a hybrid HMM-RNN.

Connectionist temporal classification