# Convolutional Variational Autoencoders for Spectrogram Compression in Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2410.02560 · HTML: https://mlsanity.com/paper/2410.02560/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech](https://mlsanity.com/paper/1803.08976/) (2018)
- [Unspeech: Unsupervised Speech Context Embeddings](https://mlsanity.com/paper/1804.06775/) (2018)

## Abstract (arXiv)

For many Automatic Speech Recognition (ASR) tasks audio features as spectrograms show better results than Mel-frequency Cepstral Coefficients (MFCC), but in practice they are hard to use due to a complex dimensionality of a feature space. The following paper presents an alternative approach towards generating compressed spectrogram representation, based on Convolutional Variational Autoencoders (VAE). A Convolutional VAE model was trained on a subsample of the LibriSpeech dataset to reconstruct short fragments of audio spectrograms (25 ms) from a 13-dimensional embedding. The trained model for a 40-dimensional (300 ms) embedding was used to generate features for corpus of spoken commands on the GoogleSpeechCommands dataset. Using the generated features an ASR system was built and compared to the model with MFCC features.