From Text to Natural Speech: Deep Learning’s Leap in Speech Synthesis
Problem
Voice is now a standard way for people to use software. Digital assistants, customer service lines, games, audiobooks, navigation and accessibility tools all need to turn written text into speech that sounds natural.
For years, text-to-speech systems were built as a chain of separate parts. One part analysed the text, another predicted pronunciation and timing, another produced acoustic features, and a final step turned those features into sound. Each part was designed and tuned on its own, often with a lot of hand-built linguistic knowledge. That made systems expensive to build and hard to adapt to a new voice or language.
Also applicable to
- Digital assistants and voice interfaces: spoken answers that sound less mechanical.
- Customer support and telephony: automated voice responses for call centres and IVR systems.
- Gaming and entertainment: character voices and narration generated from scripts.
- Media and publishing: audio versions of articles, courses and books.
- Accessibility: screen readers and assistive tools for people with visual or reading impairments.
Solution / Approach
The Elementera team's research on end-to-end speech synthesis is captured in Char2Wav, a paper co-authored by Elementera co-founder Soroush Mehri with researchers at Mila (Université de Montréal), presented at the ICLR 2017 workshop track.
Char2Wav replaces the long hand-built chain with one model that learns to go from text to audio. According to the paper, it has two components:
- A reader. This is an encoder-decoder model with attention. The encoder is a bidirectional recurrent neural network that reads text or phonemes. The decoder is a recurrent network with attention that produces acoustic features, deciding which part of the text to "look at" as it generates each step of speech.
- A neural vocoder. This turns those acoustic features into the actual sound wave. It is a conditional extension of SampleRNN, the raw-audio generation model on which Soroush Mehri is first author (ICLR 2017), so it generates the waveform one audio sample at a time.
The main difference from traditional systems, as the paper puts it, is that Char2Wav learns to produce audio directly from text. The reader and the vocoder are trained so the whole system learns from data, rather than relying on a pipeline of separately engineered steps.
Why this matters for a company using AI: an end-to-end model is easier to retrain on new data, a new speaker or a new domain, because there are fewer hand-tuned parts to rebuild.
Impact
Char2Wav was part of the early wave of neural text-to-speech research that moved the field from stitched-together pipelines to learned, end-to-end systems. For businesses, that shift means:
- Simpler voice pipelines: fewer separately built components to design, maintain and tune.
- Faster adaptation: a learned model can be trained on new recordings instead of being re-engineered by hand.
- Raw audio output: the neural vocoder generates the sound wave directly, one audio sample at a time.
- A foundation for voice products: the same building blocks support assistants, narration, dubbing and accessibility tools.
This work also gives the Elementera team first-hand experience with attention-based sequence models and raw audio generation, two ideas that sit at the heart of today's speech and generative AI systems.
Technologies
- Encoder-decoder models with attention: aligning input text with generated speech.
- Recurrent neural networks: modelling sequences of characters, phonemes and acoustic features.
- Neural vocoding (SampleRNN): generating raw audio waveforms sample by sample.
- End-to-end training: learning the full text-to-audio mapping from data.
Read the paper: Char2Wav: End-to-End Speech Synthesis (ICLR 2017 workshop). Related: SampleRNN (ICLR 2017).
Ex-Microsoft. 10+ years of experience in AI/ML, MSc from Université de Montréal (Mila). US patent holder.
AI/ML scientist with 20+ years of research & industry experience. Ex-Google DeepMind, MIT, Harvard, Mila. McGill-Mila PhD.