Combination of data borrowing strategies for low-resource LVCSR

Abstract

Large vocabulary continuous speech recognition (LVCSR) is particularly difficult for low-resource languages, where only very limited manually transcribed data are available. However, it is often feasible to obtain large amount of untranscribed data of the low-resource target language or sufficient transcribed data of some non-target languages. Borrowing data from these additional sources to help LVCSR for low-resource language becomes an important research direction. This paper presents an integrated data borrowing framework in this scenario. Three data borrowing approaches were first investigated in detail, including feature, model and data corpus. They borrow data at different levels from additional sources, and all get substantial performance improvements. As these strategies work independently, the obtained gains are likely additive. The three strategies are then combined to form an integrated data borrowing framework. Experiments showed that with the integrated data borrowing framework, significant improvement of more than 10% absolute WER reduction over a conventional baseline was obtained. In particular, the gain under the extreme limited low-resource scenario is 16%.