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Found 9 papers, 5 papers with code
Fusion approaches for emotion recognition from speech using acoustic and text-based features
In this paper, we study different approaches for classifying emotions from speech using acoustic and text-based features.
EnCodecMAE: Leveraging neural codecs for universal audio representation learning
The goal of universal audio representation learning is to obtain foundational models that can be used for a variety of downstream tasks involving speech, music or environmental sounds.
Mispronunciation detection using self-supervised speech representations
We compare two downstream approaches: 1) training the model for phone recognition (PR) using native English data, and 2) training a model directly for the target task using non-native English data.
Phone and speaker spatial organization in self-supervised speech representations
In this work, we analyze the spatial organization of phone and speaker information in several state-of-the-art speech representations using methods that do not require a downstream model.
Study of positional encoding approaches for Audio Spectrogram Transformers
Transformers have revolutionized the world of deep learning, specially in the field of natural language processing.
Simple and Cheap Setup for Timing Tapping Responses Synchronized to Auditory Stimuli
It is intended for presenting any auditory stimuli and recording tapping response times with within 2 milliseconds precision (up to -2ms lag).
Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings
Emotion recognition datasets are relatively small, making the use of the more sophisticated deep learning approaches challenging.
A Study on the Manifestation of Trust in Speech
An automatic prediction of the level of trust that a user has on a certain system could be used to attempt to correct potential distrust by having the system take relevant actions like, for example, apologizing or explaining its decisions.
Detecting Distrust Towards the Skills of a Virtual Assistant Using Speech
Research has shown that trust is an essential aspect of human-computer interaction directly determining the degree to which the person is willing to use the system.
