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.

翻译：无监督语音表征已取得显著进展，基于SUPERB、ZeroSpeech等基准测试在半监督语音识别、语音合成及纯语音语言建模方面实现了重大突破。其灵感源于对"发现语言音素"或类似低比特率编码的期望。然而，音素转录的关键特性之一是上下文不变性：语音的发音环境会极大影响其实际发音方式，但对应文本保持稳定。正是这一特性使得同一词汇的不同实例可拥有相同转录结果——这是语言理解的核心要素。现有基准测试并未评估上下文不变性。我们开发了ZeroSpeech ABX基准测试的新版本用于衡量上下文不变性，并将其应用于近期自监督表征。研究证明，表征的上下文独立性可预测词汇级表征的稳定性。建议未来研究聚焦于提升自监督与无监督表征的上下文独立性。