Despite recent advances in end-to-end speech recognition methods, their
output is biased to the training data's vocabulary, resulting in inaccurate
recognition of unknown terms or proper nouns. To improve the recognition
accuracy for a given set of such terms, we propose an adaptation parameter-free
approach based on Self-conditioned CTC. Our method improves the recognition
accuracy of misrecognized target keywords by substituting their intermediate
CTC predictions with corrected labels, which are then passed on to the
subsequent layers. First, we create pairs of correct labels and recognition
error instances for a keyword list using Text-to-Speech and a recognition
model. We use these pairs to replace intermediate prediction errors by the
labels. Conditioning the subsequent layers of the encoder on the labels, it is
possible to acoustically evaluate the target keywords. Experiments conducted in
Japanese demonstrated that our method successfully improved the F1 score for
unknown words.

基于自条件 CTC 的无适应参数方法可以通过替换中间 CTC 预测和传递纠正标签来提高对特定术语的识别准确性，实验证明该方法成功提高了未知单词的 F1 得分。