5 accepted papers
Automatic pronunciation assessment (APA) manages to evaluate the pronunciation proficiency of a second language (L2) learner in a target language. Existing efforts typically draw on regression models for proficiency score prediction, wherein the models are trained to estimate target values without e…
With the massive developments of end-to-end (E2E) neural networks, recent years have witnessed unprecedented breakthroughs in automatic speech recognition (ASR). However, the code-switching phenomenon remains a major obstacle that hinders ASR from perfection, as the lack of labeled data and the vari…
End-to-end automatic speech recognition (E2E ASR) systems often suffer from mistranscription of domain-specific phrases, such as named entities, sometimes leading to catastrophic failures in downstream tasks. A family of fast and lightweight named entity correction (NEC) models for ASR have recently…
Mispronunciation detection and diagnosis (MDD) manages to pinpoint phone-level erroneous pronunciation segmentations and provide instant and informative diagnostic feedback to L2 (second-language) learners. Among the various modeling paradigms for MDD, dictation-based neural methods have recently be…
End-to-end (E2E) neural modeling has emerged as one predominant school of thought to develop computer-assisted pronunciation training (CAPT) systems, showing competitive performance to conventional pronunciation-scoring based methods. However, current E2E neural methods for CAPT are faced with at le…