Topic
speech recognition
Researchers Analyze Fine-Tuning Strategies for Children's Speech Recognition Across Age, Gender, and Datasets
A new study from researchers including Sapkota and Narayanan provides a comprehensive analysis of fine-tuning strategies for children's automatic speech recognition (ASR), focusing on cross-dataset, age, and gender generalization. Using the TORGO database, they achieved a 4.65% relative improvement in isolated word recognition and 4.63% in sentence recognition for dysarthric speech by employing a Factorized Time Delay Neural Network (F-TDNN) with pitch features.
Improving Code-Switching ASR with Code-Mixing Guided Synthetic Speech
A new code-mixing guided preference-learning framework improves code-switching automatic speech recognition (ASR) by steering synthetic speech generation toward better language-boundary consistency. Fine-tuning Whisper Large with this approach reduced Mixed Error Rate (MER) from 12.1% to 8.9% on the DevMAN set and from 17.8% to 14.2% on the DevSGE set of the SEAME Mandarin-English conversational corpus.
Dysarthric Speech Recognition Improved by 4.65% with F-TDNN Model and Pitch Features
A systematic study by researchers from multiple institutions investigates dysarthric speech recognition using spectral features and acoustic models. The study, published on arXiv, demonstrates that incorporating pitch features and using the Factorized Time Delay Neural Network (F-TDNN) model yields a 4.65% relative improvement in isolated word recognition and a 4.63% relative improvement in sentence recognition for dysarthric speech, compared to previous research.
Improving End-to-End Speech Recognition for Dysarthric Speech through In-Domain Data Augmentation
A study by Sapkota et al. explores data augmentation techniques for dysarthric automatic speech recognition (ASR) by fine-tuning the end-to-end Wav2Vec2 model. Four methods—Speaking-Rate Modification, Pitch Modification, Formant Modification, and vocal tract Length Perturbation—were tested across severity levels, achieving relative WER reductions of 30.02%, 16.64%, and 15.47% for low, medium, and high severity respectively.
ArtNet: JEPA-Like Articulatory Framework Achieves 20.56% Error Reduction in Zero-Shot Phoneme Recognition
Researchers propose ArtNet, a JEPA-like framework for zero-shot cross-lingual phoneme recognition. By integrating an articulatory predictor with a variational information bottleneck, ArtNet suppresses language-specific variations. Experiments on seven unseen languages show a 20.56% relative reduction in phoneme error rate and 7.01% in phoneme feature error rate.