Speech recognition using deep neural networks: A systematic review
Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …
machine learning for speech processing applications, especially speech recognition …
Databases, features and classifiers for speech emotion recognition: a review
Speech is an effective medium to express emotions and attitude through language. Finding
the emotional content from a speech signal and identify the emotions from the speech …
the emotional content from a speech signal and identify the emotions from the speech …
[PDF][PDF] Improved End-to-End Speech Emotion Recognition Using Self Attention Mechanism and Multitask Learning.
Accurately recognizing emotion from speech is a necessary yet challenging task due to the
variability in speech and emotion. In this paper, we propose a speech emotion recognition …
variability in speech and emotion. In this paper, we propose a speech emotion recognition …
Attentive convolutional neural network based speech emotion recognition: A study on the impact of input features, signal length, and acted speech
Speech emotion recognition is an important and challenging task in the realm of human-
computer interaction. Prior work proposed a variety of models and feature sets for training a …
computer interaction. Prior work proposed a variety of models and feature sets for training a …
Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011
CN Anagnostopoulos, T Iliou, I Giannoukos - Artificial Intelligence Review, 2015 - Springer
Speaker emotion recognition is achieved through processing methods that include isolation
of the speech signal and extraction of selected features for the final classification. In terms of …
of the speech signal and extraction of selected features for the final classification. In terms of …
[HTML][HTML] Speech emotion recognition using fusion of three multi-task learning-based classifiers: HSF-DNN, MS-CNN and LLD-RNN
Speech emotion recognition plays an increasingly important role in emotional computing
and is still a challenging task due to its complexity. In this study, we developed a framework …
and is still a challenging task due to its complexity. In this study, we developed a framework …
Toward gender-inclusive coreference resolution
YT Cao, H Daumé III - arXiv preprint arXiv:1910.13913, 2019 - arxiv.org
Correctly resolving textual mentions of people fundamentally entails making inferences
about those people. Such inferences raise the risk of systemic biases in coreference …
about those people. Such inferences raise the risk of systemic biases in coreference …
Cross-corpus acoustic emotion recognition: Variances and strategies
As the recognition of emotion from speech has matured to a degree where it becomes
applicable in real-life settings, it is time for a realistic view on obtainable performances. Most …
applicable in real-life settings, it is time for a realistic view on obtainable performances. Most …
Paralinguistics in speech and language—state-of-the-art and the challenge
Paralinguistic analysis is increasingly turning into a mainstream topic in speech and
language processing. This article aims to provide a broad overview of the constantly …
language processing. This article aims to provide a broad overview of the constantly …
A multi-task learning framework for emotion recognition using 2D continuous space
Dimensional models have been proposed in psychology studies to represent complex
human emotional expressions. Activation and valence are two common dimensions in such …
human emotional expressions. Activation and valence are two common dimensions in such …