Unsupervised speech representations have taken off, with benchmarks (SUPERB,
ZeroSpeech) demonstrating major progress on semi-supervised speech recognition,
speech synthesis, and speech-only language modelling. Inspiration comes from
the promise of ``discovering the phonemes'' of a language or a similar
low-bitrate encoding. However, one of the critical properties of phoneme
transcriptions is context-invariance: the phonetic context of a speech sound
can have massive influence on the way it is pronounced, while the text remains
stable. This is what allows tokens of the same word to have the same
transcriptions -- key to language understanding. Current benchmarks do not
measure context-invariance. We develop a new version of the ZeroSpeech ABX
benchmark that measures context-invariance, and apply it to recent
self-supervised representations. We demonstrate that the context-independence
of representations is predictive of the stability of word-level
representations. We suggest research concentrate on improving
context-independence of self-supervised and unsupervised representations.

本文介绍了语音或文本的不变性对于理解语言至关重要，提出了一种新的 ZeroSpeech ABX 基准测试方法来测试表示的上下文独立性，并建议将重点放在改善自监督和无监督表示的上下文独立性上。