Search Results for author:
Found 4 papers, 2 papers with code
Data-Driven Mispronunciation Pattern Discovery for Robust Speech Recognition
Recent advancements in machine learning have significantly improved speech recognition, but recognizing speech from non-fluent or accented speakers remains a challenge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Paralinguistics-Aware Speech-Empowered Large Language Models for Natural Conversation
To construct USDM, we fine-tune our speech-text model on spoken dialog data using a multi-step spoken dialog template that stimulates the chain-of-reasoning capabilities exhibited by the underlying LLM.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Incorporating L2 Phonemes Using Articulatory Features for Robust Speech Recognition
Furthermore, fine-tuning on L2 speech improves recognition accuracy for both L1 and L2 speech without performance trade-offs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Unsupervised Speech Domain Adaptation Based on Disentangled Representation Learning for Robust Speech Recognition
The latent variables allow us to convert the domain of speech according to its context and domain representation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
