Embeddings: Text, Image, and Voice

Machine Learning
AI
Embeddings
Published

March 21, 2025

1 Introduction

Embeddings are a fundamental concept in machine learning, providing a way to map high-dimensional data (such as words, images, or speech) into a continuous vector space. This enables models to capture relationships and structure within the data efficiently. In this post, we’ll explore how embeddings work across text, images, and voice and compare their properties.

2 What Are Embeddings?

An embedding is a dense, lower-dimensional vector representation of an object that captures its essential features. Unlike sparse representations (e.g., one-hot encoding), embeddings help capture semantic similarities between objects. For instance:

  • Words with similar meanings have closer embeddings (e.g., king and queen).
  • Similar images have embeddings that cluster together in feature space.
  • Speech embeddings capture characteristics of the speaker and phonetic content.

3 Embeddings for Text, Images, and Voice

Different types of embeddings are used depending on the data type:

3.1 1. Text Embeddings

3.1.1 What they represent

  • Semantic meaning of words, sentences, or documents.

3.1.2 Common Approaches

  • Word2Vec (CBOW, Skip-Gram) – Learns word relationships from large corpora.
  • GloVe – Captures co-occurrence statistics.
  • FastText – Handles subword information for rare words.
  • BERT, GPT, T5 – Contextual embeddings where the same word has different representations depending on the sentence.

3.1.3 Example

  • The embeddings of king and queen are close, and vector arithmetic can approximate relationships:
    \[ \text{king} - \text{man} + \text{woman} \approx \text{queen} \]

3.2 2. Image Embeddings

3.2.1 What they represent

  • Features like edges, textures, and objects in an image.

3.2.2 Common Approaches

  • CNN-based embeddings (ResNet, VGG, EfficientNet) – Learn hierarchical feature representations.
  • Self-Supervised Learning (SimCLR, MAE, DINO) – Train without labels.
  • Vision Transformers (ViT, CLIP) – Learn image representations using transformer architectures.

3.2.3 Example

  • Face recognition models use embeddings to map multiple images of the same person to nearby vectors.

3.3 3. Voice Embeddings

3.3.1 What they represent

  • Speaker identity, phonetic content, and tone.

3.3.2 Common Approaches

  • MFCCs (Mel-Frequency Cepstral Coefficients) – Traditional feature extraction for speech.
  • Wave2Vec, WavLM, HuBERT – Self-supervised learning from raw audio.
  • Deep Speaker Models (x-vector, d-vector, ECAPA-TDNN) – Learn speaker-specific embeddings.

3.3.3 Example

  • Speaker recognition models cluster voice recordings of the same person into similar embeddings.

4 Comparison of Text, Image, and Voice Embeddings

Feature Text Embeddings Image Embeddings Voice Embeddings
Input Words, sentences Pixels, patches Waveforms, spectrograms
Model Type Transformers, RNNs, Word2Vec CNNs, ViTs, Autoencoders RNNs, CNNs, Transformers
Output Semantic vectors Feature vectors Speaker or phonetic vectors
Use Case NLP tasks (chatbots, search, summarization) Vision tasks (classification, retrieval, face recognition) Speech tasks (ASR, speaker verification)

5 Conclusion

Embeddings are crucial in modern AI, allowing models to generalize and understand relationships between different types of data. While text embeddings capture meaning and context, image embeddings learn visual features, and voice embeddings extract speaker and phonetic characteristics. Understanding embeddings is key to working with large-scale AI models across NLP, computer vision, and speech processing.

What’s next? Try using pre-trained embeddings in your ML projects! You can experiment with models like Word2Vec for NLP, CLIP for image-text similarity, or Wav2Vec for speech tasks.

Further Reading - Word2Vec: https://arxiv.org/abs/1301.3781 - BERT: https://arxiv.org/abs/1810.04805 - CLIP: https://arxiv.org/abs/2103.00020 - Wav2Vec: https://arxiv.org/abs/2006.11477