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

Autor:	Nguyen, Tu Anh, de Seyssel, Maureen, Rozé, Patricia, Rivière, Morgane, Kharitonov, Evgeny, Baevski, Alexei, Dunbar, Ewan, Dupoux, Emmanuel
Rok vydání:	2020
Předmět:	Computer Science - Computation and Language Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	We introduce a new unsupervised task, spoken language modeling: the learning of linguistic representations from raw audio signals without any labels, along with the Zero Resource Speech Benchmark 2021: a suite of 4 black-box, zero-shot metrics probing for the quality of the learned models at 4 linguistic levels: phonetics, lexicon, syntax and semantics. We present the results and analyses of a composite baseline made of the concatenation of three unsupervised systems: self-supervised contrastive representation learning (CPC), clustering (k-means) and language modeling (LSTM or BERT). The language models learn on the basis of the pseudo-text derived from clustering the learned representations. This simple pipeline shows better than chance performance on all four metrics, demonstrating the feasibility of spoken language modeling from raw speech. It also yields worse performance compared to text-based 'topline' systems trained on the same data, delineating the space to be explored by more sophisticated end-to-end models. Comment: 14 pages, including references and supplementary material
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.11588 Zobrazit plný text záznamu View this record from Arxiv