  More than half of the 7,000 languages in the world are in imminent danger of
going extinct. Traditional methods of documenting language proceed by
collecting audio data followed by manual annotation by trained linguists at
different levels of granularity. This time consuming and painstaking process
could benefit from machine learning. Many endangered languages do not have any
orthographic form but usually have speakers that are bi-lingual and trained in
a high resource language. It is relatively easy to obtain textual translations
corresponding to speech. In this work, we provide a multimodal machine learning
framework for speech representation learning by exploiting the correlations
between the two modalities namely speech and its corresponding text
translation. Here, we construct a convolutional neural network audio encoder
capable of extracting linguistic representations from speech. The audio encoder
is trained to perform a speech-translation retrieval task in a contrastive
learning framework. By evaluating the learned representations on a phone
recognition task, we demonstrate that linguistic representations emerge in the
audio encoder's internal representations as a by-product of learning to perform
the retrieval task.
