Total: 1
Automatic spoken language assessment (SLA) is a challenging problem due to the large variations in learner speech combined with limited resources. These issues are even more problematic when considering children learning a language, with higher levels of acoustic and lexical variability, and of code-switching compared to adult data. This paper describes the ALTA system for the INTERSPEECH 2020 Shared Task on Automatic Speech Recognition for Non-Native Children’s Speech. The data for this task consists of examination recordings of Italian school children aged 9–16, ranging in ability from minimal, to basic, to limited but effective command of spoken English. A variety of systems were developed using the limited training data available, 49 hours. State-of-the-art acoustic models and language models were evaluated, including a diversity of lexical representations, handling code-switching and learner pronunciation errors, and grade specific models. The best single system achieved a word error rate (WER) of 16.9% on the evaluation data. By combining multiple diverse systems, including both grade independent and grade specific models, the error rate was reduced to 15.7%. This combined system was the best performing submission for both the closed and open tasks.