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Neural Text-to-speech (TTS) synthesis is a powerful technology that cangenerate speech using neural networks. One of the most remarkable features ofTTS synthesis is its capability to produce speech in the voice of differentspeakers. This paper introduces voice cloning and speech synthesishttps://pypi.org/project/voice-cloning/ an open-source python package forhelping speech disorders to communicate more effectively as well as forprofessionals seeking to integrate voice cloning or speech synthesiscapabilities into their projects. This package aims to generate syntheticspeech that sounds like the natural voice of an individual, but it does notreplace the natural human voice. The architecture of the system comprises aspeaker verification system, a synthesizer, a vocoder, and noise reduction.Speaker verification system trained on a varied set of speakers to achieveoptimal generalization performance without relying on transcriptions.Synthesizer is trained using both audio and transcriptions that generate Melspectrogram from a text and vocoder which converts the generated MelSpectrogram into corresponding audio signal. Then the audio signal is processedby a noise reduction algorithm to eliminate unwanted noise and enhance speechclarity. The performance of synthesized speech from seen and unseen speakersare then evaluated using subjective and objective evaluation such as MeanOpinion Score (MOS), Gross Pitch Error (GPE), and Spectral distortion (SD). Themodel can create speech in distinct voices by including speaker characteristicsthat are chosen randomly.

Advancing Accessibility: Voice Cloning and Speech Synthesis for Individuals with Speech Disorders