Voice conversion (VC) refers to transforming the speaker characteristics of an utterance without altering its linguistic contents. Many works on voice conversion require to have parallel training data that is highly expensive to acquire. Recently, the cycle-consistent adversarial network (CycleGAN), which does not require parallel training data, has been applied to voice conversion, showing the state-of-the-art performance. The CycleGAN based voice conversion, however, can be used only for a pair of speakers, i.e., one-to-one voice conversion between two speakers. In this paper, we extend the CycleGAN by conditioning the network on speakers. As a result, the proposed method can perform many-to-many voice conversion among multiple speakers using a single generative adversarial network (GAN). Compared to building multiple CycleGANs for each pair of speakers, the proposed method reduces the computational and spatial cost significantly without compromising the sound quality of the converted voice. Experimental results using the VCC2018 corpus confirm the efficiency of the proposed method.

Voice activity detection (VAD) can be used to distinguish human speech from other sounds, and various applications can benefit from VAD-including speech coding and speech recognition. To accurately detect voice activity, the algorithm must take into account the characteristic features of human speech and/or background noise. In many real-life applications, noise frequently occurs in an unexpected manner, and in such situations, it is difficult to determine the characteristics of noise with sufficient accuracy. As a result, robust VAD algorithms that depend less on making correct noise estimates are desirable for real-life applications. Formants are the major spectral peaks of the human voice, and these are highly useful to distinguish vowel sounds. The characteristics of the spectral peaks are such that, these peaks are likely to survive in a signal after severe corruption by noise, and so formants are attractive features for voice activity detection under low signal-to-noise ratio (SNR) conditions. However, it is difficult to accurately extract formants from noisy signals when background noise introduces unrelated spectral peaks. Therefore, this paper proposes a simple formant-based VAD algorithm to overcome the problem of detecting formants under conditions with severe noise. The proposed method achieves a much faster processing time and outperforms standard VAD algorithms under various noise conditions. The proposed method is robust against various types of noise and produces a light computational load, so it is suitable for use in various applications.