AI text-to-speech for customer support: I developed and deployed a customizable, natural-sounding TTS model based on Tacotron2. The work spanned researching existing methods, designing the data collection process and labeling guide, performing voice cloning by fine-tuning on a proprietary dataset, and packaging the solution for production.
What Tacotron2 is
Tacotron2 is a neural text-to-speech architecture that turns raw text into speech in two stages. A sequence-to-sequence network with attention first predicts a mel-spectrogram (a compact time-frequency representation of the audio) from the input text, and a neural vocoder then converts that spectrogram into an audio waveform. Because the model learns prosody (rhythm, intonation, and stress) directly from data, it produces speech that sounds markedly more human than older concatenative or parametric systems.
Voice cloning through targeted fine-tuning
To give the system its own distinct, on-brand voice, we cloned a target voice by fine-tuning Tacotron2 on a proprietary dataset. The recordings were gathered through a data collection process built on the Prodigy annotation platform, which let us collect and label voice samples in a controlled, repeatable way rather than stitching together mismatched audio.
Consistency in the source recordings matters enormously for TTS quality, so a large part of the effort was a precise recording manual for the voice talent: exactly how to interpret punctuation as prosody, for example how long to pause on a comma versus a full stop, where to place emphasis, and how to keep tone and pacing stable across sessions. That guide is what made the collected data clean and consistent enough to fine-tune on.
Natural prosody, down to the breath
The highlight was how natural the transitions between sentences became. The model learned to reproduce the subtle in-breath a person takes before speaking, so instead of jumping abruptly from one sentence to the next, the synthesized voice “breathes” the way a human does. That single cue did a disproportionate amount of work in making the voice feel real rather than synthetic.
The result was a customizable, production-ready text-to-speech voice for customer support, natural enough that the seam between machine and human speech was genuinely hard to hear.