Designed Vocalizations Dataset: Sound-Designed Human and Animal Voices for Non-human Voice Conversion

Advances in AI-based voice conversion have enabled a wide range of media applications, including films, audiobooks, and games. However, most research and public benchmarks still focus on natural human speech, leaving designed vocalizations, such as monster growls and robotic voices, underexplored, partly due to the lack of publicly available resources. To address this gap, we introduce the Designed Vocalizations Dataset, constructed by curating diverse raw vocal sources, including speech and animal vocalizations, and applying professional vocal effects processing to produce corresponding effect modified variants. We further provide a standardized test set with explicit seen/unseen splits over source timbre groups and preset styles to assess generalization under controlled conditions. Finally, we report baseline benchmark results to support reproducible evaluation and future research. The dataset and demo samples are available at https://ncai-official.github.io/speech/publications/designed-vocalizations-dataset/.

When Humans Growl and Birds Speak: High-Fidelity Voice Conversion from Human to Animal and Designed Sounds

Human to non-human voice conversion (H2NH-VC) transforms human speech into animal or designed vocalizations. Unlike prior studies focused on dog-sounds and 16 or 22.05kHz audio transformation, this work addresses a broader range of non-speech sounds, including natural sounds (lion-roars, birdsongs) and designed voice (synthetic growls). To accomodate generation of diverse non-speech sounds and 44.1kHz high-quality audio transformation, we introduce a preprocessing pipeline and an improved CVAE-based H2NH-VC model, both optimized for human and non-human voices. Experimental results showed that the proposed method outperformed baselines in quality, naturalness, and similarity MOS, achieving effective voice conversion across diverse non-human timbres. Demo samples are available at https://nc-ai.github.io/speech/publications/nonhuman-vc/

U-Singer: Multi-Singer Singing Voice Synthesizer that Controls Emotional Intensity

We propose U-Singer, the first multi-singer emotional singing voice synthesizer that expresses various levels of emotional intensity. During synthesizing singing voices according to the lyrics, pitch, and duration of the music score, U-Singer reflects singer characteristics and emotional intensity by adding variances in pitch, energy, and phoneme duration according to singer ID and emotional intensity. Representing all attributes by conditional residual embeddings in a single unified embedding space, U-Singer controls mutually correlated style attributes, minimizing interference. Additionally, we apply emotion embedding interpolation and extrapolation techniques that lead the model to learn a linear embedding space and allow the model to express emotional intensity levels not included in the training data. In experiments, U-Singer synthesized high-fidelity singing voices reflecting the singer ID and emotional intensity. The visualization of the unified embedding space exhibits that U-singer estimates the correct variations in pitch and energy highly correlated with the singer ID and emotional intensity level. The audio samples are presented at https://u-singer.github.io.