Nonparallel Emotional Speech Conversion

We propose a nonparallel data-driven emotional speech conversion method. It enables the transfer of emotion-related characteristics of a speech signal while preserving the speaker's identity and linguistic content. Most existing approaches require parallel data and time alignment, which is not available in most real applications. We achieve nonparallel training based on an unsupervised style transfer technique, which learns a translation model between two distributions instead of a deterministic one-to-one mapping between paired examples. The conversion model consists of an encoder and a decoder for each emotion domain. We assume that the speech signal can be decomposed into an emotion-invariant content code and an emotion-related style code in latent space. Emotion conversion is performed by extracting and recombining the content code of the source speech and the style code of the target emotion. We tested our method on a nonparallel corpora with four emotions. Both subjective and objective evaluations show the effectiveness of our approach.Comment: Published in INTERSPEECH 2019, 5 pages, 6 figures. Simulation available at http://www.jian-gao.org/emoga

Tactile modulation of emotional speech samples

Copyright © 2012 Katri Salminen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly citedTraditionally only speech communicates emotions via mobile phone. However, in daily communication the sense of touch mediates emotional information during conversation. The present aim was to study if tactile stimulation affects emotional ratings of speech when measured with scales of pleasantness, arousal, approachability, and dominance. In the Experiment 1 participants rated speech-only and speech-tactile stimuli. The tactile signal mimicked the amplitude changes of the speech. In the Experiment 2 the aim was to study whether the way the tactile signal was produced affected the ratings. The tactile signal either mimicked the amplitude changes of the speech sample in question, or the amplitude changes of another speech sample. Also, concurrent static vibration was included. The results showed that the speech-tactile stimuli were rated as more arousing and dominant than the speech-only stimuli. The speech-only stimuli were rated as more approachable than the speech-tactile stimuli, but only in the Experiment 1. Variations in tactile stimulation also affected the ratings. When the tactile stimulation was static vibration the speech-tactile stimuli were rated as more arousing than when the concurrent tactile stimulation was mimicking speech samples. The results suggest that tactile stimulation offers new ways of modulating and enriching the interpretation of speech.Peer reviewe

Spoken affect classification : algorithms and experimental implementation : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University, Palmerston North, New Zealand

Machine-based emotional intelligence is a requirement for natural interaction between humans and computer interfaces and a basic level of accurate emotion perception is needed for computer systems to respond adequately to human emotion. Humans convey emotional information both intentionally and unintentionally via speech patterns. These vocal patterns are perceived and understood by listeners during conversation. This research aims to improve the automatic perception of vocal emotion in two ways. First, we compare two emotional speech data sources: natural, spontaneous emotional speech and acted or portrayed emotional speech. This comparison demonstrates the advantages and disadvantages of both acquisition methods and how these methods affect the end application of vocal emotion recognition. Second, we look at two classification methods which have gone unexplored in this field: stacked generalisation and unweighted vote. We show how these techniques can yield an improvement over traditional classification methods

Emotional State Categorization from Speech: Machine vs. Human

This paper presents our investigations on emotional state categorization from speech signals with a psychologically inspired computational model against human performance under the same experimental setup. Based on psychological studies, we propose a multistage categorization strategy which allows establishing an automatic categorization model flexibly for a given emotional speech categorization task. We apply the strategy to the Serbian Emotional Speech Corpus (GEES) and the Danish Emotional Speech Corpus (DES), where human performance was reported in previous psychological studies. Our work is the first attempt to apply machine learning to the GEES corpus where the human recognition rates were only available prior to our study. Unlike the previous work on the DES corpus, our work focuses on a comparison to human performance under the same experimental settings. Our studies suggest that psychology-inspired systems yield behaviours that, to a great extent, resemble what humans perceived and their performance is close to that of humans under the same experimental setup. Furthermore, our work also uncovers some differences between machine and humans in terms of emotional state recognition from speech.Comment: 14 pages, 15 figures, 12 table

Exploring Language-Independent Emotional Acoustic Features via Feature Selection

We propose a novel feature selection strategy to discover language-independent acoustic features that tend to be responsible for emotions regardless of languages, linguistics and other factors. Experimental results suggest that the language-independent feature subset discovered yields the performance comparable to the full feature set on various emotional speech corpora.Comment: 15 pages, 2 figures, 6 table

LinguaTag: an Emotional Speech Analysis Application

The analysis of speech, particularly for emotional content, is an open area of current research. Ongoing work has developed an emotional speech corpus for analysis, and defined a vowel stress method by which this analysis may be performed. This paper documents the development of LinguaTag, an open source speech analysis software application which implements this vowel stress emotional speech analysis method developed as part of research into the acoustic and linguistic correlates of emotional speech. The analysis output is contained within a file format combining SMIL and SSML markup tags, to facilitate search and retrieval methods within an emotional speech corpus database. In this manner, analysis performed using LinguaTag aims to combine acoustic, emotional and linguistic descriptors in a single metadata framework