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Found 15 papers, 1 papers with code
Context-sensitive evaluation of automatic speech recognition: considering user experience & language variation
Commercial Automatic Speech Recognition (ASR) systems tend to show systemic predictive bias for marginalised speaker/user groups.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition
Through a comparative experiment and a layer-wise accuracy analysis on two distinct corpora, IEMOCAP and ESD, we explore differences between AWEs and raw self-supervised representations, as well as the proper utilization of AWEs alone and in combination with word embeddings.
Quantifying the perceptual value of lexical and non-lexical channels in speech
Speech is a fundamental means of communication that can be seen to provide two channels for transmitting information: the lexical channel of which words are said, and the non-lexical channel of how they are spoken.
ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition
To overcome this challenge, we investigate how Automatic Speech Recognition (ASR) performs on emotional speech by analyzing the ASR performance on emotion corpora and examining the distribution of word errors and confidence scores in ASR transcripts to gain insight into how emotion affects ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Transfer Learning for Personality Perception via Speech Emotion Recognition
In this work, we investigate the relationship between two affective attributes: personality and emotion, from a transfer learning perspective.
Cross-Attention is Not Enough: Incongruity-Aware Dynamic Hierarchical Fusion for Multimodal Affect Recognition
Fusing multiple modalities has proven effective for multimodal information processing.
Exploration of A Self-Supervised Speech Model: A Study on Emotional Corpora
Self-supervised speech models have grown fast during the past few years and have proven feasible for use in various downstream tasks.
Analysis of Voice Conversion and Code-Switching Synthesis Using VQ-VAE
We found that speech synthesis quality degrades after increasing the number of language switches within an utterance and decreasing the number of words.
A Cross-Domain Approach for Continuous Impression Recognition from Dyadic Audio-Visual-Physio Signals
In this paper, we perform impression recognition using a proposed cross-domain architecture on the dyadic IMPRESSION dataset.
Robotic Speech Synthesis: Perspectives on Interactions, Scenarios, and Ethics
In recent years, many works have investigated the feasibility of conversational robots for performing specific tasks, such as healthcare and interview.
Fusing ASR Outputs in Joint Training for Speech Emotion Recognition
However, due to the scarcity of emotion labelled data and the difficulty of recognizing emotional speech, it is hard to obtain reliable linguistic features and models in this research area.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Location, Location: Enhancing the Evaluation of Text-to-Speech Synthesis Using the Rapid Prosody Transcription Paradigm
Text-to-Speech synthesis systems are generally evaluated using Mean Opinion Score (MOS) tests, where listeners score samples of synthetic speech on a Likert scale.
It's not what you said, it's how you said it: discriminative perception of speech as a multichannel communication system
People convey information extremely effectively through spoken interaction using multiple channels of information transmission: the lexical channel of what is said, and the non-lexical channel of how it is said.
Perception of prosodic variation for speech synthesis using an unsupervised discrete representation of F0
In English, prosody adds a broad range of information to segment sequences, from information structure (e. g. contrast) to stylistic variation (e. g. expression of emotion).
Polarity and Intensity: the Two Aspects of Sentiment Analysis
In particular, we build unimodal and multimodal multi-task learning models with sentiment score prediction as the main task and polarity and/or intensity classification as the auxiliary tasks.
