Speech-to-Singing Conversion in an Encoder-Decoder Framework
This addresses speech-to-singing conversion for applications like music production or accessibility, but it is incremental as it builds on existing encoder-decoder methods.
The paper tackles converting spoken lines to sung ones by proposing an encoder-decoder learning framework that preserves linguistic content and timbre while adhering to a target melody, achieving improved lyric intelligibility through multi-task learning.
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.