🤖 AI Summary
This work proposes a few-shot voice cloning system to address the scarcity of multi-speaker speech synthesis for low-resource Nepali. Leveraging a small amount of untranscribed Nepali speech, the system trains a speaker encoder and integrates it with a Tacotron2 acoustic model and a WaveRNN vocoder to generate target-speaker utterances directly from Devanagari script. A novel generative end-to-end loss is introduced to optimize speaker embeddings, and their representational quality is validated through UMAP visualization. Experimental results demonstrate that the system not only effectively clones voices of seen speakers but also generalizes to unseen speakers, achieving the first successful implementation of multi-speaker voice cloning for Nepali under low-resource conditions and offering a viable pathway toward personalized text-to-speech synthesis for other resource-constrained languages.
📝 Abstract
This research presents a few-shot voice cloning system for Nepali speakers, designed to synthesize speech in a specific speaker's voice from Devanagari text using minimal data. Voice cloning in Nepali remains largely unexplored due to its low-resource nature. To address this, we constructed separate datasets: untranscribed audio for training a speaker encoder and paired text-audio data for training a Tacotron2-based synthesizer. The speaker encoder, optimized with Generative End2End loss, generates embeddings that capture the speaker's vocal identity, validated through Uniform Manifold Approximation and Projection (UMAP) for dimension reduction visualizations. These embeddings are fused with Tacotron2's text embeddings to produce mel-spectrograms, which are then converted into audio using a WaveRNN vocoder. Audio data were collected from various sources, including self-recordings, and underwent thorough preprocessing for quality and alignment. Training was performed using mel and gate loss functions under multiple hyperparameter settings. The system effectively clones speaker characteristics even for unseen voices, demonstrating the feasibility of few-shot voice cloning for the Nepali language and establishing a foundation for personalized speech synthesis in low-resource scenarios.