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Stuttering is a speech disorder where the natural flow of speech isinterrupted by blocks, repetitions or prolongations of syllables, words andphrases. The majority of existing automatic speech recognition (ASR) interfacesperform poorly on utterances with stutter, mainly due to lack of matchedtraining data. Synthesis of speech with stutter thus presents an opportunity toimprove ASR for this type of speech. We describe Stutter-TTS, an end-to-endneural text-to-speech model capable of synthesizing diverse types of stutteringutterances. We develop a simple, yet effective prosody-control strategy wherebyadditional tokens are introduced into source text during training to representspecific stuttering characteristics. By choosing the position of the stuttertokens, Stutter-TTS allows word-level control of where stuttering occurs in thesynthesized utterance. We are able to synthesize stutter events with highaccuracy (F1-scores between 0.63 and 0.84, depending on stutter type). Byfine-tuning an ASR model on synthetic stuttered speech we are able to reduceword error by 5.7% relative on stuttered utterances, with only minor (<0.2%relative) degradation for fluent utterances.

Stutter-TTS: Controlled Synthesis and Improved Recognition of Stuttered Speech