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Found 12 papers, 4 papers with code
Landmark-based consonant voicing detection on multilingual corpora
Three consonant voicing classifiers were developed: (1) manually selected acoustic features anchored at a phonetic landmark, (2) MFCCs (either averaged across the segment or anchored at the landmark), and(3) acoustic features computed using a convolutional neural network (CNN).
End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager
Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utterance.
Joint Modeling of Accents and Acoustics for Multi-Accent Speech Recognition
The performance of automatic speech recognition systems degrades with increasing mismatch between the training and testing scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Deep Learning Based Speech Beamforming
On the other hand, deep learning based enhancement approaches are able to learn complicated speech distributions and perform efficient inference, but they are unable to deal with variable number of input channels.
Improved ASR for Under-Resourced Languages Through Multi-Task Learning with Acoustic Landmarks
Furui first demonstrated that the identity of both consonant and vowel can be perceived from the C-V transition; later, Stevens proposed that acoustic landmarks are the primary cues for speech perception, and that steady-state regions are secondary or supplemental.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
When CTC Training Meets Acoustic Landmarks
In this paper, the convergence properties of CTC are improved by incorporating acoustic landmarks.
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
On the other hand, CVAE training is simple but does not come with the distribution-matching property of a GAN.
REDAT: Accent-Invariant Representation for End-to-End ASR by Domain Adversarial Training with Relabeling
Accents mismatching is a critical problem for end-to-end ASR.
Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings
However, in small data regimes, we can not obtain a sufficient number of negative pairs or effectively avoid the over-fitting problem when negatives are not used at all.
LibFewShot: A Comprehensive Library for Few-shot Learning
Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmarks with various backbone architectures to evaluate common pitfalls and effects of different training tricks.
NDGGNET-A Node Independent Gate based Graph Neural Networks
Graph Neural Networks (GNNs) is an architecture for structural data, and has been adopted in a mass of tasks and achieved fabulous results, such as link prediction, node classification, graph classification and so on.
A Unified Framework for Contrastive Learning from a Perspective of Affinity Matrix
In this study, we present a new unified contrastive learning representation framework (named UniCLR) suitable for all the above four kinds of methods from a novel perspective of basic affinity matrix.
