Nonverbal vocalizations (NVs), such as laughter and sighs, are central to the expression of affective cues in emotional speech synthesis. However, learning diverse and contextually aligned NVs remains challenging in open settings due to limited NV data and the lack of explicit supervision. Motivated by this challenge, we propose Affectron as a framework for affective and contextually aligned NV generation. Built on a small-scale open and decoupled corpus, Affectron introduces an NV-augmented training strategy that expands the distribution of NV types and insertion locations. We further incorporate NV structural masking into a speech backbone pre-trained on purely verbal speech to enable diverse and natural NV synthesis. Experimental results demonstrate that Affectron produces more expressive and diverse NVs than baseline systems while preserving the naturalness of the verbal speech stream.

Recent advancements in speech synthesis have significantly improved the audio quality and pronunciation of synthesized speech. To further advance toward human-like conversational speech synthesis, this paper presents FillerSpeech, a novel speech synthesis framework that enables natural filler insertion and control over filler style. To address this, we construct a filler-inclusive speech data, derived from the open-source large-scale speech corpus. This data includes fillers with pitch and duration information. For the generation and style control of natural fillers, we propose a method that tokenizes the filler style and utilizes cross-attention with the input text. Furthermore, we introduce a large language model-based filler prediction method that enables natural insertion of fillers even when only text input is provided. The experimental results demonstrate that the constructed dataset is valid and that our proposed methods for filler style control and filler prediction are effective.