  Automatic syllable count estimation (SCE) is used in a variety of
applications ranging from speaking rate estimation to detecting social activity
from wearable microphones or developmental research concerned with quantifying
speech heard by language-learning children in different environments. The
majority of previously utilized SCE methods have relied on heuristic DSP
methods, and only a small number of bi-directional long short-term memory
(BLSTM) approaches have made use of modern machine learning approaches in the
SCE task. This paper presents a novel end-to-end method called SylNet for
automatic syllable counting from speech, built on the basis of a recent
developments in neural network architectures. We describe how the entire model
can be optimized directly to minimize SCE error on the training data without
annotations aligned at the syllable level, and how it can be adapted to new
languages using limited speech data with known syllable counts. Experiments on
several different languages reveal that SylNet generalizes to languages beyond
its training data and further improves with adaptation. It also outperforms
several previously proposed methods for syllabification, including end-to-end
BLSTMs.
