NLP plays a pivotal role in voice synthesis and speech recognition systems by enabling natural and accurate interactions. In speech recognition, NLP processes the transcribed text from audio to understand user intent, extract key entities, and generate meaningful responses. For example, converting "What’s the weather like today?" into actionable intent involves NLP.
In voice synthesis, NLP converts structured data or text into human-like speech. Text-to-speech (TTS) models, enhanced by NLP, ensure natural prosody, tone, and contextually appropriate pronunciation. Applications include virtual assistants, audiobooks, and accessibility tools for visually impaired individuals.
Advanced models like WaveNet (Google) and Tacotron combine NLP with deep learning for high-quality voice synthesis. Similarly, transformer models integrated with ASR (Automatic Speech Recognition) improve speech-to-text accuracy. Together, NLP and voice technologies are making human-computer interactions more intuitive and accessible.