voiceconversion

Voice conversion is a field within speech processing that transforms the voice of one speaker to resemble that of another without altering the spoken content. The goal is to modify speaker identity, including timbre, pitch, speaking rate, and other vocal traits, while preserving intelligibility and linguistic information.

A typical VC system extracts acoustic features from the source signal, such as spectral envelopes, fundamental frequency, and aperiodicity. A conversion model maps these source features to the target speaker’s space. The processed features are then synthesized back into a waveform. VC can be performed with parallel data (paired utterances from source and target) or non-parallel data (no direct pairs).

Common approaches include Gaussian mixture model-based mapping for classical VC, and neural methods using autoencoders, variational autoencoders, cycle-consistent networks, or generative adversarial networks. Recent work often uses neural vocoders to improve naturalness. Feature representations vary, with mel-cepstral coefficients, spectrograms, or vocoder parameters, and pitch modification to adjust F0 while maintaining natural prosody.

Evaluations rely on objective metrics such as spectral distortion and F0 error, as well as perceptual tests for naturalness and speaker similarity. Standard datasets include VCTK, CMU Arctic, and Libri-VC, with benchmarks from Voice Conversion Challenge tasks.

Applications span voice dubbing, accessibility, anonymization, and entertainment, but VC also raises ethical concerns about impersonation and consent. Robustness to recording conditions and language domains remains a challenge, as does achieving high speaker similarity without over-smoothing.