# Towards Unsupervised Automatic Speech Recognition Trained by Unaligned Speech and Text only

arXiv: https://arxiv.org/abs/1803.10952 · HTML: https://mlsanity.com/paper/1803.10952/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [Iterative pseudo-forced alignment by acoustic CTC loss for self-supervised ASR domain adaptation](https://mlsanity.com/paper/2210.15226/) (2022)
- [Improving Word Recognition in Speech Transcriptions by Decision-level Fusion of Stemming and Two-way Phoneme Pruning](https://mlsanity.com/paper/2107.12428/) (2021)
- [Temporally Aligning Long Audio Interviews with Questions: A Case Study in Multimodal Data Integration](https://mlsanity.com/paper/2310.06702/) (2023)

## Abstract (arXiv)

Automatic speech recognition (ASR) has been widely researched with supervised approaches, while many low-resourced languages lack audio-text aligned data, and supervised methods cannot be applied on them. In this work, we propose a framework to achieve unsupervised ASR on a read English speech dataset, where audio and text are unaligned. In the first stage, each word-level audio segment in the utterances is represented by a vector representation extracted by a sequence-of-sequence autoencoder, in which phonetic information and speaker information are disentangled. Secondly, semantic embeddings of audio segments are trained from the vector representations using a skip-gram model. Last but not the least, an unsupervised method is utilized to transform semantic embeddings of audio segments to text embedding space, and finally the transformed embeddings are mapped to words. With the above framework, we are towards unsupervised ASR trained by unaligned text and speech only.