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Found 10 papers, 2 papers with code
Blind Estimation of Sub-band Acoustic Parameters from Ambisonics Recordings using Spectro-Spatial Covariance Features
Additionally, we introduce FOA-Conv3D, a novel back-end network for effectively utilising the SSCV feature with a 3D convolutional encoder.
AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models
Recent advancements in Large Language Models (LLMs) have demonstrated great success in many Natural Language Processing (NLP) tasks.
A Joint Spectro-Temporal Relational Thinking Based Acoustic Modeling Framework
Despite the crucial role relational thinking plays in human understanding of speech, it has yet to be leveraged in any artificial speech recognition systems.
Dual-Constrained Dynamical Neural ODEs for Ambiguity-aware Continuous Emotion Prediction
There has been a significant focus on modelling emotion ambiguity in recent years, with advancements made in representing emotions as distributions to capture ambiguity.
Binaural Selective Attention Model for Target Speaker Extraction
The remarkable ability of humans to selectively focus on a target speaker in cocktail party scenarios is facilitated by binaural audio processing.
What is Learnt by the LEArnable Front-end (LEAF)? Adapting Per-Channel Energy Normalisation (PCEN) to Noisy Conditions
There is increasing interest in the use of the LEArnable Front-end (LEAF) in a variety of speech processing systems.
Spatial HuBERT: Self-supervised Spatial Speech Representation Learning for a Single Talker from Multi-channel Audio
Spatial HuBERT learns representations that outperform state-of-the-art single-channel speech representations on a variety of spatial downstream tasks, particularly in reverberant and noisy environments.
Variational Connectionist Temporal Classification for Order-Preserving Sequence Modeling
Connectionist temporal classification (CTC) is commonly adopted for sequence modeling tasks like speech recognition, where it is necessary to preserve order between the input and target sequences.
A Novel Markovian Framework for Integrating Absolute and Relative Ordinal Emotion Information
We propose a Markovian framework referred to as Dynamic Ordinal Markov Model (DOMM) that makes use of both absolute and relative ordinal information, to improve speech based ordinal emotion prediction.
The Ambiguous World of Emotion Representation
A key reason for this is the lack of a common mathematical framework to describe all the relevant elements of emotion representations.
