Speech

SpeechEQ: Speech Emotion Recognition based on Multi-scale Unified Datasets and Multitask Learning
Speech emotion recognition (SER) has many challenges, but one of the main challenges is that each framework does not have a unified …
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SpeechEQ: Speech Emotion Recognition based on Multi-scale Unified Datasets and Multitask Learning
Tiny-Sepformer: A Tiny Time-Domain Transformer Network For Speech Separation
Time-domain Transformer neural networks have proven their superiority in speech separation tasks. However, these models usually have a …
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Tiny-Sepformer: A Tiny Time-Domain Transformer Network For Speech Separation
Uncertainty Calibration for Deep Audio Classifiers
Although deep Neural Networks (DNNs) have achieved tremendous success in audio classification tasks, their uncertainty calibration are …
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Uncertainty Calibration for Deep Audio Classifiers
Speech Augmentation Based Unsupervised Learning for Keyword Spotting
In this paper, we investigated a speech augmentation based unsupervised learning approach for keyword spotting (KWS) task. KWS is a …
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Speech Augmentation Based Unsupervised Learning for Keyword Spotting
DT-SV: A Transformer-based Time-domain Approach for Speaker Verification
Speaker verification (SV) aims to determine whether the speaker’s identity of a test utterance is the same as the reference …
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Learning Invariant Representation and Risk Minimized for Unsupervised Accent Domain Adaptation
Unsupervised representation learning for speech audios attained impressive performances for speech recognition tasks, particularly when …
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QSpeech: Low-Qubit Quantum Speech Application Toolkit
Quantum devices with low qubits are common in the Noisy Intermediate-Scale Quantum (NISQ) era. However, Quantum Neural Network (QNN) …
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Towards Speaker Age Estimation With Label Distribution Learning
Existing methods for speaker age estimation usually treat it as a multi-class classification or a regression problem. However, precise …
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Dropout Regularization for Self-Supervised Learning of Transformer Encoder Speech Representation
Predicting the altered acoustic frames is an effective way of self-supervised learning for speech representation. However, it is …
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Dropout Regularization for Self-Supervised Learning of Transformer Encoder Speech Representation
Variational Information Bottleneck for Effective Low-Resource Audio Classification
Large-scale deep neural networks (DNNs) such as convolutional neural networks (CNNs) have achieved impressive performance in audio …
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Variational Information Bottleneck for Effective Low-Resource Audio Classification