  This paper describes a conditional neural network architecture for Mandarin
Chinese polyphone disambiguation. The system is composed of a bidirectional
recurrent neural network component acting as a sentence encoder to accumulate
the context correlations, followed by a prediction network that maps the
polyphonic character embeddings along with the conditions to corresponding
pronunciations. We obtain the word-level condition from a pre-trained
word-to-vector lookup table. One goal of polyphone disambiguation is to address
the homograph problem existing in the front-end processing of Mandarin Chinese
text-to-speech system. Our system achieves an accuracy of 94.69\% on a publicly
available polyphonic character dataset. To further validate our choices on the
conditional feature, we investigate polyphone disambiguation systems with
multi-level conditions respectively. The experimental results show that both
the sentence-level and the word-level conditional embedding features are able
to attain good performance for Mandarin Chinese polyphone disambiguation.
