  We present an approach to synthesize whisper by applying a handcrafted signal
processing recipe and Voice Conversion (VC) techniques to convert normally
phonated speech to whispered speech. We investigate using Gaussian Mixture
Models (GMM) and Deep Neural Networks (DNN) to model the mapping between
acoustic features of normal speech and those of whispered speech. We evaluate
naturalness and speaker similarity of the converted whisper on an internal
corpus and on the publicly available wTIMIT corpus. We show that applying VC
techniques is significantly better than using rule-based signal processing
methods and it achieves results that are indistinguishable from copy-synthesis
of natural whisper recordings. We investigate the ability of the DNN model to
generalize on unseen speakers, when trained with data from multiple speakers.
We show that excluding the target speaker from the training set has little or
no impact on the perceived naturalness and speaker similarity of the converted
whisper. The proposed DNN method is used in the newly released Whisper Mode of
Amazon Alexa.
