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Producing a large amount of annotated speech data for training ASR systemsremains difficult for more than 95% of languages all over the world which arelow-resourced. However, we note human babies start to learn the language by thesounds of a small number of exemplar words without hearing a large amount ofdata. We initiate some preliminary work in this direction in this paper. AudioWord2Vec is used to obtain embeddings of spoken words which carry phoneticinformation extracted from the signals. An autoencoder is used to generateembeddings of text words based on the articulatory features for the phonemesequences. Both sets of embeddings for spoken and text words describe similarphonetic structures among words in their respective latent spaces. A mappingrelation from the audio embeddings to text embeddings actually gives theword-level ASR. This can be learned by aligning a small number of spoken wordsand the corresponding text words in the embedding spaces. In the initialexperiments only 200 annotated spoken words and one hour of speech data withoutannotation gave a word accuracy of 27.5%, which is low but a good startingpoint.

Almost-unsupervised Speech Recognition with Close-to-zero Resource Based on Phonetic Structures Learned from Very Small Unpaired Speech and Text Data