  Stuttering is a speech disorder where the natural flow of speech is
interrupted by blocks, repetitions or prolongations of syllables, words and
phrases. The majority of existing automatic speech recognition (ASR) interfaces
perform poorly on utterances with stutter, mainly due to lack of matched
training data. Synthesis of speech with stutter thus presents an opportunity to
improve ASR for this type of speech. We describe Stutter-TTS, an end-to-end
neural text-to-speech model capable of synthesizing diverse types of stuttering
utterances. We develop a simple, yet effective prosody-control strategy whereby
additional tokens are introduced into source text during training to represent
specific stuttering characteristics. By choosing the position of the stutter
tokens, Stutter-TTS allows word-level control of where stuttering occurs in the
synthesized utterance. We are able to synthesize stutter events with high
accuracy (F1-scores between 0.63 and 0.84, depending on stutter type). By
fine-tuning an ASR model on synthetic stuttered speech we are able to reduce
word error by 5.7% relative on stuttered utterances, with only minor (<0.2%
relative) degradation for fluent utterances.
