  In this paper, we propose an end-to-end Mandarin tone classification method
from continuous speech utterances utilizing both the spectrogram and the short
term context information as the inputs. Both Mel-spectrograms and context
segment features are used to train the tone classifier. We first divide the
spectrogram frames into syllable segments using force alignment results
produced by an ASR model. Then we extract the short term segment features to
capture the context information across multiple syllables. Feeding both the
Mel-spectrogram and the short term context segment features into an end-to-end
model could significantly improve the performance. Experiments are performed on
a large scale open source Mandarin speech dataset to evaluate the proposed
method. Results show that the this method improves the classification accuracy
from $79.5\%$ to $88.7\%$ on the AISHELL3 database.
