Reference 4 stops to get here

Text-to-Speech

Synthesizing natural-sounding speech from text, using neural vocoders and attention-based models.

Your route here

4 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. Feature ✓ understood

    A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.

  3. Feature Engineering ✓ understood

    The process of selecting, transforming, and creating input features to improve model performance.

  4. Audio Processing ✓ understood

    Techniques for analyzing, transforming, and understanding audio signals for tasks like speech recognition and music generation.

  5. Text-to-Speech · you are here ✓ understood

Where it sits

Before this

Audio Processing
Text-to-Speech

Leads to

Nothing yet: a destination in its own right.

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Language & LLMs Speech Recognition Converting spoken language into text using acoustic models and language models, now dominated by deep learning. Language & LLMs Autoregressive Model A model that generates output one token at a time, using previously generated tokens as input for the next prediction. Language & LLMs Sequence-to-Sequence Models that transform input sequences to output sequences, used for translation, summarization, and generation. Vision & Multimodal Multimodal Model Models processing multiple data types (text, images, audio) jointly, like GPT-4V, Gemini, or CLIP. Language & LLMs Transformer A neural network architecture, introduced in 2017, built from stacked self-attention and feed-forward layers; the basis of nearly every modern large language model.

In the research

All papers →

3 papers that build on Text-to-Speech .