  The identification of syllables within phonetic sequences is known as
syllabification. This task is thought to play an important role in natural
language understanding, speech production, and the development of speech
recognition systems. The concept of the syllable is cross-linguistic, though
formal definitions are rarely agreed upon, even within a language. In response,
data-driven syllabification methods have been developed to learn from
syllabified examples. These methods often employ classical machine learning
sequence labeling models. In recent years, recurrence-based neural networks
have been shown to perform increasingly well for sequence labeling tasks such
as named entity recognition (NER), part of speech (POS) tagging, and chunking.
We present a novel approach to the syllabification problem which leverages
modern neural network techniques. Our network is constructed with long
short-term memory (LSTM) cells, a convolutional component, and a conditional
random field (CRF) output layer. Existing syllabification approaches are rarely
evaluated across multiple language families. To demonstrate cross-linguistic
generalizability, we show that the network is competitive with state of the art
systems in syllabifying English, Dutch, Italian, French, Manipuri, and Basque
datasets.
