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Found 6 papers, 0 papers with code
Improving Speech Recognition Error Prediction for Modern and Off-the-shelf Speech Recognizers
In this work, we extend a prior phonetic confusion based model for predicting speech recognition errors in two ways: first, we introduce a sampling-based paradigm that better simulates the behavior of a posterior-based acoustic model.
Ultra-lightweight Neural Differential DSP Vocoder For High Quality Speech Synthesis
A DSP vocoder often gets a lower audio quality due to consuming over-smoothed acoustic model predictions of approximate representations for the vocal tract.
Synthetic Cross-accent Data Augmentation for Automatic Speech Recognition
In this work, we improve an accent-conversion model (ACM) which transforms native US-English speech into accented pronunciation.
Voice-preserving Zero-shot Multiple Accent Conversion
Most people who have tried to learn a foreign language would have experienced difficulties understanding or speaking with a native speaker's accent.
Building an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data
As it is difficult to collect spoken data from users without a functioning SLU system, our method does not rely on spoken data for training, rather we use an ASR error predictor to "speechify" the text data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Hallucination of speech recognition errors with sequence to sequence learning
Automatic Speech Recognition (ASR) is an imperfect process that results in certain mismatches in ASR output text when compared to plain written text or transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
