Finding the Most Uniform Changes in Vowel Polygon Caused by Psychological Stress

Using vowel polygons, exactly their parameters, is chosen as the criterion for achievement of differences between normal state of speaker and relevant speech under real psychological stress. All results were experimentally obtained by created software for vowel polygon analysis applied on ExamStress database. Selected 6 methods based on cross-correlation of different features were classified by the coefficient of variation and for each individual vowel polygon, the efficiency coefficient marking the most significant and uniform differences between stressed and normal speech were calculated. As the best method for observing generated differences resulted method considered mean of cross correlation values received for difference area value with vector length and angle parameter couples. Generally, best results for stress detection are achieved by vowel triangles created by /i/-/o/-/u/ and /a/-/i/-/o/ vowel triangles in formant planes containing the fifth formant F5 combined with other formants

Learning Representations of Emotional Speech with Deep Convolutional Generative Adversarial Networks

Automatically assessing emotional valence in human speech has historically been a difficult task for machine learning algorithms. The subtle changes in the voice of the speaker that are indicative of positive or negative emotional states are often "overshadowed" by voice characteristics relating to emotional intensity or emotional activation. In this work we explore a representation learning approach that automatically derives discriminative representations of emotional speech. In particular, we investigate two machine learning strategies to improve classifier performance: (1) utilization of unlabeled data using a deep convolutional generative adversarial network (DCGAN), and (2) multitask learning. Within our extensive experiments we leverage a multitask annotated emotional corpus as well as a large unlabeled meeting corpus (around 100 hours). Our speaker-independent classification experiments show that in particular the use of unlabeled data in our investigations improves performance of the classifiers and both fully supervised baseline approaches are outperformed considerably. We improve the classification of emotional valence on a discrete 5-point scale to 43.88% and on a 3-point scale to 49.80%, which is competitive to state-of-the-art performance

Alcohol Language Corpus

The Alcohol Language Corpus (ALC) is the first publicly available speech corpus comprising intoxicated and sober speech of 162 female and male German speakers. Recordings are done in the automotive environment to allow for the development of automatic alcohol detection and to ensure a consistent acoustic environment for the alcoholized and the sober recording. The recorded speech covers a variety of contents and speech styles. Breath and blood alcohol concentration measurements are provided for all speakers. A transcription according to SpeechDat/Verbmobil standards and disfluency tagging as well as an automatic phonetic segmentation are part of the corpus. An Emu version of ALC allows easy access to basic speech parameters as well as the us of R for statistical analysis of selected parts of ALC. ALC is available without restriction for scientific or commercial use at the Bavarian Archive for Speech Signals

Fluency in dialogue: Turn‐taking behavior shapes perceived fluency in native and nonnative speech

Fluency is an important part of research on second language learning, but most research on language proficiency typically has not included oral fluency as part of interaction, even though natural communication usually occurs in conversations. The present study considered aspects of turn-taking behavior as part of the construct of fluency and investigated whether these aspects differentially influence perceived fluency ratings of native and non-native speech. Results from two experiments using acoustically manipulated speech showed that, in native speech, too ‘eager’ (interrupting a question with a fast answer) and too ‘reluctant’ answers (answering slowly after a long turn gap) negatively affected fluency ratings. However, in non-native speech, only too ‘reluctant’ answers led to lower fluency ratings. Thus, we demonstrate that acoustic properties of dialogue are perceived as part of fluency. By adding to our current understanding of dialogue fluency, these lab-based findings carry implications for language teaching and assessmen

SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure

Authentication of Students and Students’ Work in E-Learning : Report for the Development Bid of Academic Year 2010/11

Global e-learning market is projected to reach $107.3 billion by 2015 according to a new report by The Global Industry Analyst (Analyst 2010). The popularity and growth of the online programmes within the School of Computer Science obviously is in line with this projection. However, also on the rise are students’ dishonesty and cheating in the open and virtual environment of e-learning courses (Shepherd 2008). Institutions offering e-learning programmes are facing the challenges of deterring and detecting these misbehaviours by introducing security mechanisms to the current e-learning platforms. In particular, authenticating that a registered student indeed takes an online assessment, e.g., an exam or a coursework, is essential for the institutions to give the credit to the correct candidate. Authenticating a student is to ensure that a student is indeed who he says he is. Authenticating a student’s work goes one step further to ensure that an authenticated student indeed does the submitted work himself. This report is to investigate and compare current possible techniques and solutions for authenticating distance learning student and/or their work remotely for the elearning programmes. The report also aims to recommend some solutions that fit with UH StudyNet platform.Submitted Versio

Introducing a corpus of conversational stories. Construction and annotation of the Narrative Corpus

Although widely seen as critical both in terms of its frequency and its social significance as a prime means of encoding and perpetuating moral stance and configuring self and identity, conversational narrative has received little attention in corpus linguistics. In this paper we describe the construction and annotation of a corpus that is intended to advance the linguistic theory of this fundamental mode of everyday social interaction: the Narrative Corpus (NC). The NC contains narratives extracted from the demographically-sampled sub-corpus of the British National Corpus (BNC) (XML version). It includes more than 500 narratives, socially balanced in terms of participant sex, age, and social class. We describe the extraction techniques, selection criteria, and sampling methods used in constructing the NC. Further, we describe four levels of annotation implemented in the corpus: speaker (social information on speakers), text (text Ids, title, type of story, type of embedding etc.), textual components (pre-/post-narrative talk, narrative, and narrative-initial/final utterances), and utterance (participation roles, quotatives and reporting modes). A brief rationale is given for each level of annotation, and possible avenues of research facilitated by the annotation are sketched out