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Voice Conversion (VC) emerged as a significant domain of research in thefield of speech synthesis in recent years due to its emerging application invoice-assisting technology, automated movie dubbing, and speech-to-singingconversion to name a few. VC basically deals with the conversion of vocal styleof one speaker to another speaker while keeping the linguistic contentsunchanged. VC task is performed through a three-stage pipeline consisting ofspeech analysis, speech feature mapping, and speech reconstruction. Nowadaysthe Generative Adversarial Network (GAN) models are widely in use for speechfeature mapping from source to target speaker. In this paper, we propose anadaptive learning-based GAN model called ALGAN-VC for an efficient one-to-oneVC of speakers. Our ALGAN-VC framework consists of some approaches to improvethe speech quality and voice similarity between source and target speakers. Themodel incorporates a Dense Residual Network (DRN) like architecture to thegenerator network for efficient speech feature learning, for source to targetspeech feature conversion. We also integrate an adaptive learning mechanism tocompute the loss function for the proposed model. Moreover, we use a boostedlearning rate approach to enhance the learning capability of the proposedmodel. The model is trained by using both forward and inverse mappingsimultaneously for a one-to-one VC. The proposed model is tested on VoiceConversion Challenge (VCC) 2016, 2018, and 2020 datasets as well as on ourself-prepared speech dataset, which has been recorded in Indian regionallanguages and in English. A subjective and objective evaluation of thegenerated speech samples indicated that the proposed model elegantly performedthe voice conversion task by achieving high speaker similarity and adequatespeech quality.

An Adaptive Learning based Generative Adversarial Network for One-To-One Voice Conversion