Syntactic and Semantic Features For Code-Switching Factored Language Models
This work addresses the challenge of accurate speech recognition for bilingual speakers who mix languages, though it appears incremental as it builds on existing factored language model approaches.
The paper tackled the problem of improving automatic speech recognition for Code-Switching speech by integrating syntactic and semantic features into factored language models, resulting in a 10.8% relative reduction in perplexity and a 3.4% relative reduction in mixed error rate on the SEAME Mandarin-English corpus.
This paper presents our latest investigations on different features for factored language models for Code-Switching speech and their effect on automatic speech recognition (ASR) performance. We focus on syntactic and semantic features which can be extracted from Code-Switching text data and integrate them into factored language models. Different possible factors, such as words, part-of-speech tags, Brown word clusters, open class words and clusters of open class word embeddings are explored. The experimental results reveal that Brown word clusters, part-of-speech tags and open-class words are the most effective at reducing the perplexity of factored language models on the Mandarin-English Code-Switching corpus SEAME. In ASR experiments, the model containing Brown word clusters and part-of-speech tags and the model also including clusters of open class word embeddings yield the best mixed error rate results. In summary, the best language model can significantly reduce the perplexity on the SEAME evaluation set by up to 10.8% relative and the mixed error rate by up to 3.4% relative.