  We describe and analyze a simple and effective algorithm for sequence
segmentation applied to speech processing tasks. We propose a neural
architecture that is composed of two modules trained jointly: a recurrent
neural network (RNN) module and a structured prediction model. The RNN outputs
are considered as feature functions to the structured model. The overall model
is trained with a structured loss function which can be designed to the given
segmentation task. We demonstrate the effectiveness of our method by applying
it to two simple tasks commonly used in phonetic studies: word segmentation and
voice onset time segmentation. Results sug- gest the proposed model is superior
to previous methods, ob- taining state-of-the-art results on the tested
datasets.
