Search Results for author:
Found 12 papers, 1 papers with code
EM-Network: Oracle Guided Self-distillation for Sequence Learning
We introduce EM-Network, a novel self-distillation approach that effectively leverages target information for supervised sequence-to-sequence (seq2seq) learning.
MCR-Data2vec 2.0: Improving Self-supervised Speech Pre-training via Model-level Consistency Regularization
Self-supervised learning (SSL) has shown significant progress in speech processing tasks.
Development of deep biological ages aware of morbidity and mortality based on unsupervised and semi-supervised deep learning approaches
Methods: This paper proposes a novel deep learning model to learn latent representations of biological aging in regard to subjects' morbidity and mortality.
Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition
Recently, the advance in deep learning has brought a considerable improvement in the end-to-end speech recognition field, simplifying the traditional pipeline while producing promising results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
HuBERT-EE: Early Exiting HuBERT for Efficient Speech Recognition
Pre-training with self-supervised models, such as Hidden-unit BERT (HuBERT) and wav2vec 2. 0, has brought significant improvements in automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models
Extending this supervised scheme further, we introduce a new type of teacher model for connectionist temporal classification (CTC)-based sequence models, namely Oracle Teacher, that leverages both the source inputs and the output labels as the teacher model's input.
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition
To reduce this computational burden, knowledge distillation (KD), which is a popular model compression method, has been used to transfer knowledge from a deep and complex model (teacher) to a shallower and simpler model (student).
Speech to Text Adaptation: Towards an Efficient Cross-Modal Distillation
We transfer the knowledge from a concrete Transformer-based text LM to an SLU module which can face a data shortage, based on recent cross-modal distillation methodologies.
Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency
This paper suggests a system which identifies the inherent intention of a spoken utterance given its transcript, in some cases using auxiliary acoustic features.
An Efficient Model Selection for Gaussian Mixture Model in a Bayesian Framework
In order to cluster or partition data, we often use Expectation-and-Maximization (EM) or Variational approximation with a Gaussian Mixture Model (GMM), which is a parametric probability density function represented as a weighted sum of $\hat{K}$ Gaussian component densities.
Statistical Denoising for single molecule fluorescence microscopic images
Single molecule fluorescence microscopy is a powerful technique for uncovering detailed information about biological systems, both in vitro and in vivo.
Efficient Estimation of the number of neighbours in Probabilistic K Nearest Neighbour Classification
Probabilistic k-nearest neighbour (PKNN) classification has been introduced to improve the performance of original k-nearest neighbour (KNN) classification algorithm by explicitly modelling uncertainty in the classification of each feature vector.
