description Tacotron 2 Overview
Tacotron 2 is a neural network architecture for text-to-speech (TTS) synthesis introduced by Google researchers in 2017. The system operates by splitting the process into two stages: a sequence-to-sequence model predicts mel-scale spectrograms from input text, and a modified WaveNet model acts as a vocoder to generate raw audio from those spectrograms. This combination achieved notable naturalness, producing speech that closely resembled human recordings. It is intended for machine learning researchers and developers building high-quality voice interfaces.
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What is Tacotron 2 designed to do?
Tacotron 2 is a neural network architecture designed for text-to-speech (TTS) synthesis. It was introduced by Google researchers in 2017. The system takes written text and generates highly natural-sounding human speech from it.
What are the two main stages of the Tacotron 2 system?
The system operates by splitting the process into two distinct stages. First, a sequence-to-sequence model predicts mel-scale spectrograms from the input text. Second, a modified WaveNet model acts as a vocoder to convert those spectrograms into actual audio waveforms.
How does the speech generated by Tacotron 2 sound compared to older models?
The speech produced by Tacotron 2 is significantly more natural and human-like than older, concatenative TTS systems. It effectively captures prosody, intonation, and subtle pauses in speech. This was a major breakthrough in reducing the robotic sound typical of early text-to-speech engines.
Who introduced the Tacotron 2 architecture?
The Tacotron 2 model was developed and introduced by researchers at Google in late 2017. They published a paper detailing the architecture alongside samples of its impressive audio output. The underlying WaveNet vocoder was originally developed by Google's DeepMind division.
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