6 accepted papers
Automatic syllable stress detection is a crucial component in Computer-Assisted Language Learning (CALL) systems for language learners. Current stress detection models are typically trained on clean speech, which may not be robust in real-world scenarios where background noise is prevalent. To addre…
Automatic syllable stress detection is an essential component in Computer assisted language learning (CALL) systems to guide nonnative language learners. In English, each word typically contains only one primary stressed syllable. However, standard loss functions, such as Binary Cross-Entropy (BCE),…
With the availability of large data, ASRs perform well on native English but poorly for non-native English data. Training nonnative ASRs or adapting a native English ASR is often limited by the availability of data, particularly for low resource scenarios. A typical HMM-DNN based ASR decoding requir…
Spoken language training benefits from showing a video of native speakers' articulatory movements to train the second language learners. Typically, the articulatory video is prepared in conjunction with the audio which is collected simultaneously with the articulatory recording. Articulatory video r…
Automatic syllable stress detection is useful in assessing and diagnosing the quality of the pronunciation of second language (L2) learners in an automated way. Typically, the syllable stress depends on three prominence measures - intensity level, duration, pitch - around the sound unit with the hig…
A typical solution for the speech rate estimation consists of two stages, which involves first computing a short-time feature contour such that most of peaks of the contour correspond to the syllable nuclei followed by the detection of the peaks of the contour corresponding to the syllable nuclei. T…