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Found 14 papers, 4 papers with code
Improving fairness for spoken language understanding in atypical speech with Text-to-Speech
Spoken language understanding (SLU) systems often exhibit suboptimal performance in processing atypical speech, typically caused by neurological conditions and motor impairments.
Time Scale Network: A Shallow Neural Network For Time Series Data
The network simultaneously learns features at many time scales for sequence classification with significantly reduced parameters and operations.
Leveraging Pretrained Image-text Models for Improving Audio-Visual Learning
Cascaded SpeechCLIP attempted to generate localized word-level information and utilize both the pretrained image and text encoders.
DuTa-VC: A Duration-aware Typical-to-atypical Voice Conversion Approach with Diffusion Probabilistic Model
We present a novel typical-to-atypical voice conversion approach (DuTa-VC), which (i) can be trained with nonparallel data (ii) first introduces diffusion probabilistic model (iii) preserves the target speaker identity (iv) is aware of the phoneme duration of the target speaker.
Regularizing Contrastive Predictive Coding for Speech Applications
These representations significantly reduce the amount of labeled data needed for downstream task performance, such as automatic speech recognition.
Stabilized training of joint energy-based models and their practical applications
The JEM training relies on "positive examples" (i. e. examples from the training data set) as well as on "negative examples", which are samples from the modeled distribution $p(x)$ generated by means of Stochastic Gradient Langevin Dynamics (SGLD).
Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech
In recent studies, self-supervised pre-trained models tend to outperform supervised pre-trained models in transfer learning.
Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations
This paper applies a non-contrastive self-supervised learning method on an unlabeled speech corpus to learn utterance-level embeddings.
Unsupervised Speech Segmentation and Variable Rate Representation Learning using Segmental Contrastive Predictive Coding
We overcome this limitation with a segmental contrastive predictive coding (SCPC) framework to model the signal structure at a higher level, e. g., phone level.
Beyond Isolated Utterances: Conversational Emotion Recognition
While most of the current approaches focus on inferring emotion from isolated utterances, we argue that this is not sufficient to achieve conversational emotion recognition (CER) which deals with recognizing emotions in conversations.
Pathological voice adaptation with autoencoder-based voice conversion
This approach alleviates the evaluation problem one normally has when converting typical speech to pathological speech, as in our approach, the voice conversion (VC) model does not need to be optimised for speech degradation but only for the speaker change.
Segmental Contrastive Predictive Coding for Unsupervised Word Segmentation
We overcome this limitation with a segmental contrastive predictive coding (SCPC) framework that can model the signal structure at a higher level e. g. at the phoneme level.
Artificial Intelligence applied to chest X-Ray images for the automatic detection of COVID-19. A thoughtful evaluation approach
Current standard protocols used in the clinic for diagnosing COVID-19 include molecular or antigen tests, generally complemented by a plain chest X-Ray.
CopyPaste: An Augmentation Method for Speech Emotion Recognition
Data augmentation is a widely used strategy for training robust machine learning models.
