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Found 6 papers, 5 papers with code
Rejecting Cognitivism: Computational Phenomenology for Deep Learning
We propose a non-representationalist framework for deep learning relying on a novel method: computational phenomenology, a dialogue between the first-person perspective (relying on phenomenology) and the mechanisms of computational models.
BYOL-S: Learning Self-supervised Speech Representations by Bootstrapping
Our results indicate that the hybrid model with a convolutional transformer as the encoder yields superior performance in most HEAR challenge tasks.
Ranked #1 on
Self-Supervised Learning
on CREMA-D
Hybrid Handcrafted and Learnable Audio Representation for Analysis of Speech Under Cognitive and Physical Load
To that end, we introduce a set of five datasets for task load detection in speech.
SERAB: A multi-lingual benchmark for speech emotion recognition
To facilitate the process, here, we present the Speech Emotion Recognition Adaptation Benchmark (SERAB), a framework for evaluating the performance and generalization capacity of different approaches for utterance-level SER.
Word-level Embeddings for Cross-Task Transfer Learning in Speech Processing
Recent breakthroughs in deep learning often rely on representation learning and knowledge transfer.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
Deep speech inpainting of time-frequency masks
To address these limitations, here we propose an end-to-end framework for speech inpainting, the context-based retrieval of missing or severely distorted parts of time-frequency representation of speech.
