  We propose an Encoder-Classifier framework to model the Mandarin tones using
recurrent neural networks (RNN). In this framework, extracted frames of
features for tone classification are fed in to the RNN and casted into a fixed
dimensional vector (tone embedding) and then classified into tone types using a
softmax layer along with other auxiliary inputs. We investigate various
configurations that help to improve the model, including pooling, feature
splicing and utilization of syllable-level tone embeddings. Besides, tone
embeddings and durations of the contextual syllables are exploited to
facilitate tone classification. Experimental results on Mandarin tone
classification show the proposed network setups improve tone classification
accuracy. The results indicate that the RNN encoder-classifier based tone model
flexibly accommodates heterogeneous inputs (sequential and segmental) and hence
has the advantages from both the sequential classification tone models and
segmental classification tone models.
