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We introduce a new unsupervised task, spoken language modeling: the learningof linguistic representations from raw audio signals without any labels, alongwith the Zero Resource Speech Benchmark 2021: a suite of 4 black-box, zero-shotmetrics probing for the quality of the learned models at 4 linguistic levels:phonetics, lexicon, syntax and semantics. We present the results and analysesof a composite baseline made of the concatenation of three unsupervisedsystems: self-supervised contrastive representation learning (CPC), clustering(k-means) and language modeling (LSTM or BERT). The language models learn onthe basis of the pseudo-text derived from clustering the learnedrepresentations. This simple pipeline shows better than chance performance onall four metrics, demonstrating the feasibility of spoken language modelingfrom raw speech. It also yields worse performance compared to text-based'topline' systems trained on the same data, delineating the space to beexplored by more sophisticated end-to-end models.

The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling