This paper proposes a new voice conversion (VC) task from human speech to
dog-like speech while preserving linguistic information as an example of human
to non-human creature voice conversion (H2NH-VC) tasks. Although most VC
studies deal with human to human VC, H2NH-VC aims to convert human speech into
non-human creature-like speech. Non-parallel VC allows us to develop H2NH-VC,
because we cannot collect a parallel dataset that non-human creatures speak
human language. In this study, we propose to use dogs as an example of a
non-human creature target domain and define the "speak like a dog" task. To
clarify the possibilities and characteristics of the "speak like a dog" task,
we conducted a comparative experiment using existing representative
non-parallel VC methods in acoustic features (Mel-cepstral coefficients and
Mel-spectrograms), network architectures (five different kernel-size settings),
and training criteria (variational autoencoder (VAE)- based and generative
adversarial network-based). Finally, the converted voices were evaluated using
mean opinion scores: dog-likeness, sound quality and intelligibility, and
character error rate (CER). The experiment showed that the employment of the
Mel-spectrogram improved the dog-likeness of the converted speech, while it is
challenging to preserve linguistic information. Challenges and limitations of
the current VC methods for H2NH-VC are highlighted.

本文提出了从人类语音到狗狗语音的语音转换任务，并尝试使用 Mel-spectrogram 来保持狗狗语音的相似度。