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Found 8 papers, 0 papers with code
Progressive unsupervised domain adaptation for ASR using ensemble models and multi-stage training
A previous study has shown the effectiveness of using ensemble teacher models in T/S training for unsupervised domain adaptation (UDA) but its performance still lags behind compared to the model trained on in-domain data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
MUST: A Multilingual Student-Teacher Learning approach for low-resource speech recognition
However, a limitation of KD training is that the student model classes must be a proper or improper subset of the teacher model classes.
Learning Cross-lingual Mappings for Data Augmentation to Improve Low-Resource Speech Recognition
The results show that any source language ASR model can be used for a low-resource target language recognition followed by proposed mapping model.
Ensemble CNNs for Breast Tumor Classification
To improve the recognition ability of computer-aided breast mass classification among mammographic images, in this work we explore the state-of-the-art classification networks to develop an ensemble mechanism.
Towards domain generalisation in ASR with elitist sampling and ensemble knowledge distillation
Knowledge distillation has widely been used for model compression and domain adaptation for speech applications.
Non-Linear Pairwise Language Mappings for Low-Resource Multilingual Acoustic Model Fusion
A separate regression neural network is trained for each source-target language pair to transform posteriors from source acoustic model to the target language.
Investigating the Impact of Cross-lingual Acoustic-Phonetic Similarities on Multilingual Speech Recognition
This technique measures the similarities between posterior distributions from various monolingual acoustic models against a target speech signal.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding
The downside of multishot MRI is that it is very sensitive to subject motion and even small amounts of motion during the scan can produce artifacts in the final MR image that may cause misdiagnosis.
Generative Adversarial Network
Motion Correction In Multishot Mri
+1
