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[Arianne Bennett]

AI voice generators use deep learning techniques to synthesize human-like speech from text. Here’s a breakdown of how they work:

1. Text Processing (Text-to-Phoneme Conversion)

• The input text is analyzed and converted into a phonetic representation.
• Natural Language Processing (NLP) is used to understand sentence structure, punctuation, and prosody (rhythm and intonation).

2. Acoustic Model

• A deep learning model (such as a neural network) predicts the audio features needed to generate realistic speech.
• This includes aspects like pitch, tone, and cadence.

3. Speech Synthesis

• There are two primary methods used:
  • Concatenative Synthesis: Uses pre-recorded speech segments and stitches them together.
  • Parametric Synthesis: Uses AI to generate speech waveform from scratch based on learned speech patterns.

4. Waveform Generation

• Models like WaveNet (by Google DeepMind) or Tacotron generate high-quality, human-like voices.
• These models create raw audio waveforms that sound natural and fluid.

5. Post-Processing & Fine-Tuning

• Additional filters and optimizations improve clarity and reduce noise.
• Some models allow customization, such as adjusting speed, pitch, or emotional tone.