Standard 5 stops to get here

Speech Recognition

Converting spoken language into text using acoustic models and language models, now dominated by deep learning.

Your route here

5 stops · basics first
  1. Natural Language Processing ✓ understood

    The field of AI that lets computers read, interpret, translate and generate human language, from spam filters and search to chatbots.

  2. Dataset ✓ understood

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

  3. 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.

  4. Feature Engineering ✓ understood

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

  5. Audio Processing ✓ understood

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

  6. Speech Recognition · you are here ✓ understood

Where it sits

Before this

Audio Processing Natural Language Processing
Speech Recognition

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Training CTC Loss Connectionist Temporal Classification loss for sequence tasks without alignment, used in speech recognition. Vision & Multimodal Text-to-Speech Synthesizing natural-sounding speech from text, using neural vocoders and attention-based models. Language & LLMs Sequence-to-Sequence Models that transform input sequences to output sequences, used for translation, summarization, and generation. Language & LLMs Language Modeling Learning probability distributions over sequences of words to predict what comes next. 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 →

2 papers that build on Speech Recognition .