From c89c5a7299c96c0493489dbf210a9f785eabd7a8 Mon Sep 17 00:00:00 2001 From: Dongji Gao Date: Mon, 25 Sep 2023 14:38:03 -0400 Subject: [PATCH] Update README.md --- egs/librispeech/WSASR/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/egs/librispeech/WSASR/README.md b/egs/librispeech/WSASR/README.md index b7cbae304..0a06c7984 100644 --- a/egs/librispeech/WSASR/README.md +++ b/egs/librispeech/WSASR/README.md @@ -35,7 +35,7 @@ We modify $G(\mathbf{y})$ by adding self-loop arcs into each state and bypass ar We incorporate the penalty strategy and apply different configurations for the self-loop arc and bypass arc. The penalties are set as - $\lambda_{1_{i}} = \beta_{1} * \tau_{1}^{i},\quad \lambda_{2_{i}} = \beta_{2} * \tau_{2}^{i}$ +$$\lambda_{1_{i}} = \beta_{1} * \tau_{1}^{i},\quad \lambda_{2_{i}} = \beta_{2} * \tau_{2}^{i}$$ for the $i$-th training epoch. $\beta$ is the initial penalty that encourages the model to rely more on the given transcript at the start of training. It decays exponentially by a factor of $\tau \in (0, 1)$, gradually encouraging the model to align speech with $\star$ when getting confused.