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Found 5 papers, 1 papers with code
Almost-unsupervised Speech Recognition with Close-to-zero Resource Based on Phonetic Structures Learned from Very Small Unpaired Speech and Text Data
This can be learned by aligning a small number of spoken words and the corresponding text words in the embedding spaces.
Phonetic-and-Semantic Embedding of Spoken Words with Applications in Spoken Content Retrieval
Stage 1 performs phonetic embedding with speaker characteristics disentangled.
Towards Unsupervised Automatic Speech Recognition Trained by Unaligned Speech and Text only
In this work, we propose a framework to achieve unsupervised ASR on a read English speech dataset, where audio and text are unaligned.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Language Transfer of Audio Word2Vec: Learning Audio Segment Representations without Target Language Data
We train SA from one language (source language) and use it to extract the vector representation of the audio segments of another language (target language).
Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder
The vector representations of fixed dimensionality for words (in text) offered by Word2Vec have been shown to be very useful in many application scenarios, in particular due to the semantic information they carry.
