“THIS IS A STUNNING, STUNNING NIGHT”: NEWS MEDIA CONSTRUCTIONS OF EMOTIONAL REALITY

The news media is one of the main influences of people’s perception, especially during elections. It can have an influence on a voter’s perception during and after the results of an election. One of the issues that arises is that news reports are biased, which affects viewers’ perceptions and interpretations of the information reported. This paper presents an analysis of Critical Discourse Analysis (CDA) in the genre of news media and further shows that prosodic features is another layer of analysis in CDA. I am looking specifically at how news broadcasts construct information after the 2016 election results. News media that represent either conservative liberal or Right-wing both use strategies to manipulate information even when the viewer wants to think of news media as neutral. In this paper, nine news reports that represent either conservative liberal or Right-wing from four leading news broadcasts were analyzed: Fox News, ABC News, NBC News, and CNN Breaking News. A transcription key organized the notes made of the micro element patterns seen in each report, which the patterns are color-coded. Observations were made through differences in the visual content shown, word choices, face-threatening acts, phonetic features, and prosodic features. Vowels and intonation was analyzed through the program Praat, a free computer software package for the scientific analysis of speech in phonetics, which spectrograms are included to show the patterns that are evident. The patterns revealed that news media presents information based on the side they represent and leave out information that contradicts their representation of reality. The phonetic features also show that there is a construction of emotional speech, which in turn affects how the audience perceives the information

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Influence of expressive speech on ASR performances: application to elderly assistance in smart home

International audienceSmart homes are discussed as a win-win solution for maintaining the Elderly at home as a better alternative to care homes for dependent elderly people. Such Smart homes are characterized by rich domestic commands devoted to elderly safety and comfort. The vocal command has been identified as an efficient , well accepted, interaction way, it can be directly addressed to the "habitat", or through a robotic interface. In daily use, the challenges of vocal commands recognition are the noisy environment but moreover the reformulation and the expressive change of the strictly authorized commands. This paper focuses (1) to show, on the base of elicited corpus, that expressive speech, in particular distress speech, strongly affects generic state of the art ASR systems (20 to 30%) (2) how interesting improvement thanks to ASR adaptation can regulate (15%) this degradation. We conclude on the necessary adaptation of ASR system to expressive speech when they are designed for person's assistance

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

Evolutionary and Cognitive Approaches to Voice Perception in Humans: Acoustic Properties, Personality and Aesthetics

Voices are used as a vehicle for language, and variation in the acoustic properties of voices also contains information about the speaker. Listeners use measurable qualities, such as pitch and formant traits, as cues to a speaker’s physical stature and attractiveness. Emotional states and personality characteristics are also judged from vocal stimuli. The research contained in this thesis examines vocal masculinity, aesthetics and personality, with an emphasis on the perception of prosocial traits including trustworthiness and cooperativeness. I will also explore themes which are more cognitive in nature, testing aspects of vocal stimuli which may affect trait attribution, memory and the ascription of identity. Chapters 2 and 3 explore systematic differences across vocal utterances, both in types of utterance using different classes of stimuli and across the time course of perception of the auditory signal. These chapters examine variation in acoustic measurements in addition to variation in listener attributions of commonly-judged speaker traits. The most important result from this work was that evaluations of attractiveness made using spontaneous speech correlated with those made using scripted speech recordings, but did not correlate with those made of the same persons using vowel stimuli. This calls into question the use of sustained vowel sounds for the attainment of ratings of subjective characteristics. Vowel and single-word stimuli are also quite short – while I found that attributions of masculinity were reliable at very short exposure times, more subjective traits like attractiveness and trustworthiness require a longer exposure time to elicit reliable attributions. I conclude with recommending an exposure time of at least 5 seconds in duration for such traits to be reliably assessed. Chapter 4 examines what vocal traits affect perceptions of pro-social qualities using both natural and manipulated variation in voices. While feminine pitch traits (F0 and F0-SD) were linked to cooperativeness ratings, masculine formant traits (Df and Pf) were also associated with cooperativeness. The relative importance of these traits as social signals is discussed. Chapter 5 questions what makes a voice memorable, and helps to differentiate between memory for individual voice identities and for the content which was spoken by administering recognition tests both within and across sensory modalities. While the data suggest that experimental manipulation of voice pitch did not influence memory for vocalised stimuli, attractive male voices were better remembered than unattractive voices, independent of pitch manipulation. Memory for cross-modal (textual) content was enhanced by raising the voice pitch of both male and female speakers. I link this pattern of results to the perceived dominance of voices which have been raised and lowered in pitch, and how this might impact how memories are formed and retained. Chapter 6 examines masculinity across visual and auditory sensory modalities using a cross-modal matching task. While participants were able to match voices to muted videos of both male and female speakers at rates above chance, and to static face images of men (but not women), differences in masculinity did not influence observers in their judgements, and voice and face masculinity were not correlated. These results are discussed in terms of the generally-accepted theory that masculinity and femininity in faces and voices communicate the same underlying genetic quality. The biological mechanisms by which vocal and facial masculinity could develop independently are speculated

Detecting emotions from speech using machine learning techniques

D.Phil. (Electronic Engineering

Multimodaalsel emotsioonide tuvastamisel põhineva inimese-roboti suhtluse arendamine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneÜks afektiivse arvutiteaduse peamistest huviobjektidest on mitmemodaalne emotsioonituvastus, mis leiab rakendust peamiselt inimese-arvuti interaktsioonis. Emotsiooni äratundmiseks uuritakse nendes süsteemides nii inimese näoilmeid kui kakõnet. Käesolevas töös uuritakse inimese emotsioonide ja nende avaldumise visuaalseid ja akustilisi tunnuseid, et töötada välja automaatne multimodaalne emotsioonituvastussüsteem. Kõnest arvutatakse mel-sageduse kepstri kordajad, helisignaali erinevate komponentide energiad ja prosoodilised näitajad. Näoilmeteanalüüsimiseks kasutatakse kahte erinevat strateegiat. Esiteks arvutatakse inimesenäo tähtsamate punktide vahelised erinevad geomeetrilised suhted. Teiseks võetakse emotsionaalse sisuga video kokku vähendatud hulgaks põhikaadriteks, misantakse sisendiks konvolutsioonilisele tehisnärvivõrgule emotsioonide visuaalsekseristamiseks. Kolme klassifitseerija väljunditest (1 akustiline, 2 visuaalset) koostatakse uus kogum tunnuseid, mida kasutatakse õppimiseks süsteemi viimasesetapis. Loodud süsteemi katsetati SAVEE, Poola ja Serbia emotsionaalse kõneandmebaaside, eNTERFACE’05 ja RML andmebaaside peal. Saadud tulemusednäitavad, et võrreldes olemasolevatega võimaldab käesoleva töö raames loodudsüsteem suuremat täpsust emotsioonide äratundmisel. Lisaks anname käesolevastöös ülevaate kirjanduses väljapakutud süsteemidest, millel on võimekus tunda äraemotsiooniga seotud ̆zeste. Selle ülevaate eesmärgiks on hõlbustada uute uurimissuundade leidmist, mis aitaksid lisada töö raames loodud süsteemile ̆zestipõhiseemotsioonituvastuse võimekuse, et veelgi enam tõsta süsteemi emotsioonide äratundmise täpsust.Automatic multimodal emotion recognition is a fundamental subject of interest in affective computing. Its main applications are in human-computer interaction. The systems developed for the foregoing purpose consider combinations of different modalities, based on vocal and visual cues. This thesis takes the foregoing modalities into account, in order to develop an automatic multimodal emotion recognition system. More specifically, it takes advantage of the information extracted from speech and face signals. From speech signals, Mel-frequency cepstral coefficients, filter-bank energies and prosodic features are extracted. Moreover, two different strategies are considered for analyzing the facial data. First, facial landmarks' geometric relations, i.e. distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames. Then they are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to the key-frames summarizing the videos. Afterward, the output confidence values of all the classifiers from both of the modalities are used to define a new feature space. Lastly, the latter values are learned for the final emotion label prediction, in a late fusion. The experiments are conducted on the SAVEE, Polish, Serbian, eNTERFACE'05 and RML datasets. The results show significant performance improvements by the proposed system in comparison to the existing alternatives, defining the current state-of-the-art on all the datasets. Additionally, we provide a review of emotional body gesture recognition systems proposed in the literature. The aim of the foregoing part is to help figure out possible future research directions for enhancing the performance of the proposed system. More clearly, we imply that incorporating data representing gestures, which constitute another major component of the visual modality, can result in a more efficient framework