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Found 9 papers, 2 papers with code
Contrastive Siamese Network for Semi-supervised Speech Recognition
This paper introduces contrastive siamese (c-siam) network, an architecture for leveraging unlabeled acoustic data in speech recognition.
Analyzing Large Receptive Field Convolutional Networks for Distant Speech Recognition
In the present study, we address this issue by investigating variants of large receptive field CNNs (LRF-CNNs) which include deeply recursive networks, dilated convolutional neural networks, and stacked hourglass networks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Domain Expansion in DNN-based Acoustic Models for Robust Speech Recognition
An obvious approach to leverage data from a new domain (e. g., new accented speech) is to first generate a comprehensive dataset of all domains, by combining all available data, and then use this dataset to retrain the acoustic models.
Probabilistic Permutation Invariant Training for Speech Separation
Recently proposed Permutation Invariant Training (PIT) addresses this problem by determining the output-label assignment which minimizes the separation error.
Jointly Aligning and Predicting Continuous Emotion Annotations
To deal with this challenge, we introduce a new convolutional neural network (multi-delay sinc network) that is able to simultaneously align and predict labels in an end-to-end manner.
Convolutional Neural Network-based Speech Enhancement for Cochlear Implant Recipients
To improve speech enhancement methods for CI users, we propose to perform speech enhancement in a cochlear filter-bank feature space, a feature-set specifically designed for CI users based on CI auditory stimuli.
Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain
We introduce trainable time warping (TTW), whose complexity is linear in both the number and the length of time-series.
Capturing Long-term Temporal Dependencies with Convolutional Networks for Continuous Emotion Recognition
The goal of continuous emotion recognition is to assign an emotion value to every frame in a sequence of acoustic features.
Progressive Neural Networks for Transfer Learning in Emotion Recognition
Many paralinguistic tasks are closely related and thus representations learned in one domain can be leveraged for another.
