Digital Einstein Experience: Fast Text-to-Speech for Conversational AI
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Abstract (arXiv)
We describe our approach to create and deliver a custom voice for a conversational AI use-case. More specifically, we provide a voice for a Digital Einstein character, to enable human-computer interaction within the digital conversation experience. To create the voice which fits the context well, we first design a voice character and we produce the recordings which correspond to the desired speech attributes. We then model the voice. Our solution utilizes Fastspeech 2 for log-scaled mel-spectrogram prediction from phonemes and Parallel WaveGAN to generate the waveforms. The system supports a character input and gives a speech waveform at the output. We use a custom dictionary for selected words to ensure their proper pronunciation. Our proposed cloud architecture enables for fast voice delivery, making it possible to talk to the digital version of Albert Einstein in real-time.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 5 references that are in the map. First posted on arXiv 21 Jul 2021.
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References · 5
- 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 804
- 2020FastSpeech 2: Fast and High-Quality End-to-End Text to Speechcited by 717
- 2019Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogramcited by 340
- 2017Parallel WaveNet: Fast High-Fidelity Speech Synthesiscited by 215
- 2020FastPitch: Parallel Text-to-speech with Pitch Predictioncited by 151
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