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This paper presents the design, implementation and evaluation of a speechediting system, named EditSpeech, which allows a user to perform deletion,insertion and replacement of words in a given speech utterance, without causingaudible degradation in speech quality and naturalness. The EditSpeech system isdeveloped upon a neural text-to-speech (NTTS) synthesis framework. Partialinference and bidirectional fusion are proposed to effectively incorporate thecontextual information related to the edited region and achieve smoothtransition at both left and right boundaries. Distortion introduced to theunmodified parts of the utterance is alleviated. The EditSpeech system isdeveloped and evaluated on English and Chinese in multi-speaker scenarios.Objective and subjective evaluation demonstrate that EditSpeech outperforms afew baseline systems in terms of low spectral distortion and preferred speechquality. Audio samples are available online for demonstrationhttps://daxintan-cuhk.github.io/EditSpeech/ .

EditSpeech: A Text Based Speech Editing System Using Partial Inference and Bidirectional Fusion