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Found 12 papers, 1 papers with code
Exploring the Integration of E2E ASR and Pronunciation Modeling for English Mispronunciation Detection
In the first stage, the speech uttered by an L2 learner is processed by an end-to-end ASR module to produce N-best phone sequence hypotheses.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition
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.
Preserving Phonemic Distinctions for Ordinal Regression: A Novel Loss Function for Automatic Pronunciation Assessment
Automatic pronunciation assessment (APA) manages to quantify the pronunciation proficiency of a second language (L2) learner in a language.
AVATAR: Robust Voice Search Engine Leveraging Autoregressive Document Retrieval and Contrastive Learning
Voice, as input, has progressively become popular on mobiles and seems to transcend almost entirely text input.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Effective Cross-Utterance Language Modeling for Conversational Speech Recognition
Conversational speech normally is embodied with loose syntactic structures at the utterance level but simultaneously exhibits topical coherence relations across consecutive utterances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Exploring Non-Autoregressive End-To-End Neural Modeling For English Mispronunciation Detection And Diagnosis
In addition, we design and develop a pronunciation modeling network stacked on top of the NAR E2E models of our method to further boost the effectiveness of MD&D.
Maximum F1-score training for end-to-end mispronunciation detection and diagnosis of L2 English speech
End-to-end (E2E) neural models are increasingly attracting attention as a promising modeling approach for mispronunciation detection and diagnosis (MDD).
TENET: A Time-reversal Enhancement Network for Noise-robust ASR
Due to the unprecedented breakthroughs brought about by deep learning, speech enhancement (SE) techniques have been developed rapidly and play an important role prior to acoustic modeling to mitigate noise effects on speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
End-to-End Mispronunciation Detection and Diagnosis From Raw Waveforms
Furthermore, our model can achieve comparable mispronunciation detection performance in relation to state-of-the-art E2E MDD models that take input the standard handcrafted acoustic features.
An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling
Mispronunciation detection and diagnosis (MDD) is a core component of computer-assisted pronunciation training (CAPT).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
