Pronunciation assessment and its application in computer-aided pronunciation training
(CAPT) have seen impressive progress in recent years. With the rapid growth in language …

Deep learning-based pronunciation scoring models highly rely on the availability of the
annotated non-native data, which is costly and has scalability issues. To deal with the data …

Abstract Neural Networks (NN) and Gaussian Processes (GP) are different modelling
approaches. The former stores characteristics of the training data in its many parameters …

Automatic non-native fluency scoring is a challenging task which relies heavily on the
effectiveness of the handcrafted fluency features used for predicting fluency scores. In this …

A typical fluency scoring system generally relies on an automatic speech recognition (ASR)
system to obtain time stamps in input speech for the subsequent calculation of fluency …

Recent studies on pronunciation scoring have explored the effect of introducing phone
embeddings as reference pronunciation, but mostly in an implicit manner, ie, addition or …

Automatic spoken language assessment often operates within a regime whereby only a
limited quantity of training data is available. In other low-resourced tasks, such as in speech …

Automatic pronunciation assessment is commonly trained and applied for a specific
language, which is not practical in multi-lingual or low-resource scenarios. In this paper, we …

Empirical studies report a strong correlation between pronunciation proficiency scores and
phonetic errors in non-native speech assessments of human evaluators. However, the …

The automatic fluency assessment of spontaneous speech without reference text is a
challenging task that heavily depends on the accuracy of automatic speech recognition …