  In this paper our goal is to convert a set of spoken lines into sung ones.
Unlike previous signal processing based methods, we take a learning based
approach to the problem. This allows us to automatically model various aspects
of this transformation, thus overcoming dependence on specific inputs such as
high quality singing templates or phoneme-score synchronization information.
Specifically, we propose an encoder--decoder framework for our task. Given
time-frequency representations of speech and a target melody contour, we learn
encodings that enable us to synthesize singing that preserves the linguistic
content and timbre of the speaker while adhering to the target melody. We also
propose a multi-task learning based objective to improve lyric intelligibility.
We present a quantitative and qualitative analysis of our framework.
