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Found 5 papers, 1 papers with code
Phonetic and Prosody-aware Self-supervised Learning Approach for Non-native Fluency Scoring
Specifically, we first pre-train the model using a reconstruction loss function, by masking phones and their durations jointly on a large amount of unlabeled speech and text prompts.
Leveraging phone-level linguistic-acoustic similarity for utterance-level pronunciation scoring
Recent studies on pronunciation scoring have explored the effect of introducing phone embeddings as reference pronunciation, but mostly in an implicit manner, i. e., addition or concatenation of reference phone embedding and actual pronunciation of the target phone as the phone-level pronunciation quality representation.
An ASR-free Fluency Scoring Approach with Self-Supervised Learning
A typical fluency scoring system generally relies on an automatic speech recognition (ASR) system to obtain time stamps in input speech for either the subsequent calculation of fluency-related features or directly modeling speech fluency with an end-to-end approach.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Improving Non-native Word-level Pronunciation Scoring with Phone-level Mixup Data Augmentation and Multi-source Information
Moreover, we utilize multi-source information (e. g., MFCC and deep features) to further improve the scoring system performance.
A Full Text-Dependent End to End Mispronunciation Detection and Diagnosis with Easy Data Augmentation Techniques
Recently, end-to-end mispronunciation detection and diagnosis (MD&D) systems has become a popular alternative to greatly simplify the model-building process of conventional hybrid DNN-HMM systems by representing complicated modules with a single deep network architecture.
