3 accepted papers
In this paper, we investigate the benefit that off-the-shelf word embedding can bring to the sequence-to-sequence (seq-to-seq) automatic speech recognition (ASR). We first introduced the word embedding regularization by maximizing the cosine similarity between a transformed decoder feature and the t…
Lack of code-switching data is an issue of training codeswitching language model. In this paper, we propose an approach to train code-switching language models with monolingual data only. By constraining and normalizing output projection matrix in RNN based language model, we make the embeddings of…
Speech-to-text translation (ST) refers to transforming the audio in source language to the text in target language. Mainstream solutions for such tasks are to cascade automatic speech recognition with machine translation, for which the transcriptions of the source language are needed in training. En…