LPM

2000년 도입 · 논문 17편에서 사용

Local Prior Matching is a semi-supervised objective for speech recognition that distills knowledge from a strong prior (e.g. a language model) to provide learning signal to a discriminative model trained on unlabeled speech. The LPM objective minimizes the cross entropy between the local prior and the model distribution, and is minimized when $q\_{y\mid{x}} = \bar{p}\_{y\mid{x}}$. Intuitively, LPM encourages the ASR model to assign posterior probabilities proportional to the linguistic probabilities of the proposed hypotheses.

출처: Semi-Supervised Speech Recognition via Local Prior Matching

소개 논문: Semi-Supervised Speech Recognition via Local Prior Matching