Sabda2Baachan

Abstract

This work proposes a novel neural architecture Sabda2Baachan for text-to-speech synthesis capable of producing high-quality speech with natural prosody and speaker characteristics. The model employs a multi-stream approach, where distinct components predict various low-level prosodic features, including energy, pitch, and duration. The proposed model demonstrated superior performance compared to several state-of-the-art models, achieving remarkable naturalness, intelligibility, and speaker similarity in the synthesized speech.

Evaluation Metrics:

• PERCEPTUAL EVALUATION OF SPEECH QUALITY (PESQ)
• SIGNAL TO DISTORTION RATIO (SDR)
• SIGNAL-TO-NOISE RATIO (SNR)
• SHORT-TIME OBJECTIVE INTELLIGIBILITY (STOI)

Text-to-Speech with Different SOTA Models

Models like Tortoise, Bark are compared with Sabda2Baachan

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Text-to-Speech with Different Custom Models within Group Members

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