Evaluating context-invariance in unsupervised speech representations
This work addresses a gap in evaluating unsupervised speech representations for researchers in speech processing, but it is incremental as it builds on existing benchmarks without introducing a new method.
The authors tackled the problem of evaluating context-invariance in unsupervised speech representations, which is not measured by current benchmarks, by developing a new version of the ZeroSpeech ABX benchmark and applying it to recent self-supervised representations, showing that context-independence predicts the stability of word-level representations.
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.