  Emotional and controllable speech synthesis is a topic that has received much
attention. However, most studies focused on improving the expressiveness and
controllability in the context of linguistic content, even though natural
verbal human communication is inseparable from spontaneous non-speech
expressions such as laughter, crying, or grunting. We propose a model called
LaughNet for synthesizing laughter by using waveform silhouettes as inputs. The
motivation is not simply synthesizing new laughter utterances, but testing a
novel synthesis-control paradigm that uses an abstract representation of the
waveform. We conducted basic listening test experiments, and the results showed
that LaughNet can synthesize laughter utterances with moderate quality and
retain the characteristics of the training example. More importantly, the
generated waveforms have shapes similar to the input silhouettes. For future
work, we will test the same method on other types of human nonverbal
expressions and integrate it into more elaborated synthesis systems.
