  In this paper, we present a new method for recognizing tones in continuous
speech for tonal languages. The method works by converting the speech signal to
a cepstrogram, extracting a sequence of cepstral features using a convolutional
neural network, and predicting the underlying sequence of tones using a
connectionist temporal classification (CTC) network. The performance of the
proposed method is evaluated on a freely available Mandarin Chinese speech
corpus, AISHELL-1, and is shown to outperform the existing techniques in the
literature in terms of tone error rate (TER).
