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

Abstract

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.

14 pages, including references and supplementary material

Cited by in corpus (9)

• Self-Supervised Speech Representation Learning: A Review
• A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion
• Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge
• Contrastive prediction strategies for unsupervised segmentation and categorization of phonemes and words
• Probing phoneme, language and speaker information in unsupervised speech representations
• BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models
• Speech foundation models on intelligibility prediction for hearing-impaired listeners
• Allophant: Cross-lingual Phoneme Recognition with Articulatory Attributes
• DP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon