Persian Speech Emotion Recognition Approach based on Multilayer Perceptron

The present paper is focused on the study of pre-service student’s attitude towards use of helpful technology in teaching learning process. A descriptive and survey method was used for the study. The sample consists of 150 pre-service students at B.ED and M.ED levels in the Department of Education, Aligarh Muslim University, Aligarh. Out of 150 BED and MED students 69 were male and the rest female. We adapted tools from the research work of Abani Gwanshak Shikded and Theresa Ledger and further the tools were modified according to the objective of the study. We developed a tool by ourselves to measure attitude of pre- service student’s towards use of helpful technology in teaching learning process to disabled children. The data was tabulated and systematically analyzed, with the help of the Microsoft Excel. The data was fed in the Excel sheet and then analyzed using operations like converting the data into percentage, addition etc. and interpreted on the basis of objectives of the study. We took five types of helpful technology for various disabilities, namely, helpful technology for “visually impaired, reading impaired, hearing impaired, writing impaired and mathematically impaired” . The major findings of the study revealed that majority of the students are aware of helpful technology but they are not skilled in using helpful technology in teaching learning process and also majority of the pre-service students have favourable attitude towards the use of helpful technology in teaching learning process

You 'Have' to Hear This: Using Tone of Voice to Motivate Others.

The present studies explored the role of prosody in motivating others, and applied self-determination theory (Ryan & Deci, 2000) to do so. Initial studies describe patterns of prosody that discriminate motivational speech. Autonomy support was expressed with lower intensity, slower speech rate and less voice energy in both motivationally laden and neutral (but motivationally primed) sentences. In a follow-up study, participants were able to recognize motivational prosody in semantically neutral sentences, suggesting prosody alone may carry motivational content. Findings from subsequent studies also showed that an autonomy-supportive as compared with a controlling tone facilitated positive personal (perceived choice and lower perceived pressure, well-being) and interpersonal (closeness to others and prosocial behaviors) outcomes commonly linked to this type of motivation. Results inform both the social psychology (in particular motivation) and psycho-linguistic (in particular prosody) literatures and offer a first description of how motivational tone alone can shape listeners' experiences. (PsycINFO Database Recor

"Time Slows Down Whenever You Are Around" for Women but Not for Men.

What happens when we unexpectedly see an attractive potential partner? Previous studies in laboratory settings suggest that the visualization of attractive and unattractive photographs influences the perception of time. The major aim of this research is to study time perception and attraction in a realistic social scenario, by investigating if changes in subjective time measured during a speed dating are associated with attraction. The duration of the dates was variable and participants had to estimate the time that passed. Among other measures, participants also rated the potential partners in terms of their physical attractiveness before and after the dates and reported if they would like to exchange contact with them. Results showed that, in a real speed dating situation, when there is a perception of the partner as being physically more attractive, women tend to overestimate the duration of that meeting, whereas men tend to underestimate its duration. Such changes may reflect evolutionary adaptations which make the human cognitive system more responsive in situations related to reproductive fitness

It doesn’t hurt to ask: Question-asking increases liking.

Conversation is a fundamental human experience, one that is necessary to pursue intrapersonal and interpersonal goals across myriad contexts, relationships, and modes of communication. In the current research, we isolate the role of an understudied conversational behavior: question-asking. Across three studies of live dyadic conversations, we identify a robust and consistent relationship between question-asking and liking: people who ask more questions are better liked by their conversation partners. When people are instructed to ask more questions, they are perceived as higher in responsiveness, an interpersonal construct that captures listening, understanding, validation, and care. We measure responsiveness with an attitudinal measure from previous research as well as a novel behavioral measure: the number of follow-up questions one asks. In both cases, responsiveness explains the effect of question-asking on liking. In addition to analyzing live get-to-know-you conversations online, we also studied face-to-face speed-dating conversations. We find that speed daters who ask more questions during their dates are more likely to elicit agreement for second dates from their partners, a behavioral indicator of liking. We trained a natural language processing algorithm as a “follow-up question detector” that we applied to our speed-dating data (and can be applied to any text data to more deeply understand question-asking dynamics). The follow-up question rate established by the algorithm explained why question-asking led to speed-dating success. We also find that, despite the persistent and beneficial effects of asking questions, people do not anticipate that question-asking increases interpersonal liking

A survey on perceived speaker traits: personality, likability, pathology, and the first challenge

The INTERSPEECH 2012 Speaker Trait Challenge aimed at a unified test-bed for perceived speaker traits – the first challenge of this kind: personality in the five OCEAN personality dimensions, likability of speakers, and intelligibility of pathologic speakers. In the present article, we give a brief overview of the state-of-the-art in these three fields of research and describe the three sub-challenges in terms of the challenge conditions, the baseline results provided by the organisers, and a new openSMILE feature set, which has been used for computing the baselines and which has been provided to the participants. Furthermore, we summarise the approaches and the results presented by the participants to show the various techniques that are currently applied to solve these classification tasks

Paralinguistic event detection in children's speech

Paralinguistic events are useful indicators of the affective state of a speaker. These cues, in children's speech, are used to form social bonds with their caregivers. They have also been found to be useful in the very early detection of developmental disorders such as autism spectrum disorder (ASD) in children's speech. Prior work on children's speech has focused on the use of a limited number of subjects which don't have sufficient diversity in the type of vocalizations that are produced. Also, the features that are necessary to understand the production of paralinguistic events is not fully understood. To account for the lack of an off-the-shelf solution to detect instances of laughter and crying in children's speech, the focus of the thesis is to investigate and develop signal processing algorithms to extract acoustic features and use machine learning algorithms on various corpora. Results obtained using baseline spectral and prosodic features indicate the ability of the combination of spectral, prosodic, and dysphonation-related features that are needed to detect laughter and whining in toddlers' speech with different age groups and recording environments. The use of long-term features were found to be useful to capture the periodic properties of laughter in adults' and children's speech and detected instances of laughter to a high degree of accuracy. Finally, the thesis focuses on the use of multi-modal information using acoustic features and computer vision-based smile-related features to detect instances of laughter and to reduce the instances of false positives in adults' and children's speech. The fusion of the features resulted in an improvement of the accuracy and recall rates than when using either of the two modalities on their own.Ph.D

Influence Level Prediction on Social Media through Multi-Task and Sociolinguistic User Characteristics Modeling

Prediction of a user’s influence level on social networks has attracted a lot of attention as human interactions move online. Influential users have the ability to influence others’ behavior to achieve their own agenda. As a result, predicting users’ level of influence online can help to understand social networks, forecast trends, prevent misinformation, etc. The research on user influence in social networks has attracted much attention across multiple disciplines, from social sciences to mathematics, yet it is still not well understood. One of the difficulties is that the definition of influence is specific to a particular problem or a domain, and it does not generalize well. Another challenge arises from the fact that all user interactions occur through text. Textual data limits access to non-verbal communication such as voice. These facts make the problem challenging. In this work, we define user influence level as a function of community endorsement, create a strong baseline, and develop new methods that significantly outperform our baseline by leveraging demographic and personality data. This dissertation is divided into three parts. In part one, we introduce the problem of influence level prediction, review influential research across different disciplines, and introduce our hypothesis that leverages user-centric information to improve user influence level prediction on social media. In part two, we answer the question of whether the language provides sufficient information to predict user- related information. We develop new methods that achieve good results on three tasks: relationship prediction, demographic prediction, and hedge sentence detection. In part three, we introduce our dataset, a new ranking algorithm, RankDCG, to assess the performance of ranking problems, and develop new user-centric models for user influence level prediction. These models show significant improvements across eight different domains ranging from politics and news to fitness

Cross-regional word duration patterns in Mandarin

Duration contrasts can convey many types of information, including language background, word structure, word frequency, speech genre, intention, and emotions. An understanding of duration lays the foundation for many aspects of speech technology since duration plays a major role in speech production and perception. This dissertation explores the duration patterns of Mandarin words among three Mandarin dialectal regions---Beijing, Taiwan, and Malaysia. This dissertation brings diverse methodologies on speech data collection, annotation, and corpus construction to investigate linguistic pattern. Three speech production studies are conducted to explore the duration patterns of words with different length and internal structures. These studies reveal the general duration patterns of Mandarin Words. First of all, all the multi-syllabic words demonstrate the disyllabic long-short metrical form. Second, linguistic factors---syllable structure, positions (syllable position, word position, and sentence position), word frequency, word category, word internal structure, particle attachment, speech rate of sentence have significant effects on syllable duration. Thirdly, social factor---region interacts with multiple linguistic factors (word structure, syllable position, and particle attachment) and plays an important role in duration prediction. Quantitative data from these studies reveal that there are regional differences in rhythmic contrast among different Mandarin speaking regions. Beijing Mandarin speakers are more sensitive to the length change of linguistic unit and show stronger rhythmic contrast than speakers from Taiwan and Malaysia Mandarins. The results also display that Malaysia Mandarin speakers show the similar rhythmic pattern as Beijing Mandarin speakers. The investigation of duration patterns in this dissertation provides a detailed description of word duration in Mandarin. This dissertation also provides the foundation for further research on duration pattern related super-segmental feature. A comprehensive understanding of duration pattern with linguistic and social factors is helpful to improve the quality of durational models used in speech technology

Deception detection in dialogues

In the social media era, it is commonplace to engage in written conversations. People sometimes even form connections across large distances, in writing. However, human communication is in large part non-verbal. This means it is now easier for people to hide their harmful intentions. At the same time, people can now get in touch with more people than ever before. This puts vulnerable groups at higher risk for malevolent interactions, such as bullying, trolling, or predatory behavior. Furthermore, such growing behaviors have most recently led to waves of fake news and a growing industry of deceit creators and deceit detectors. There is now an urgent need for both theory that explains deception and applications that automatically detect deception. In this thesis I address this need with a novel application that learns from examples and detects deception reliably in natural-language dialogues. I formally define the problem of deception detection and identify several domains where it is useful. I introduce and evaluate new psycholinguistic features of deception in written dialogues for two datasets. My results shed light on the connection between language, deception, and perception. They also underline the challenges and difficulty of assessing perceptions from written text. To automatically learn to detect deception I first introduce an expressive logical model and then present a probabilistic model that simplifies the first and is learnable from labeled examples. I introduce a belief-over-belief formalization, based on Kripke semantics and situation calculus. I use an observation model to describe how utterances are produced from the nested beliefs and intentions. This allows me to easily make inferences about these beliefs and intentions given utterances, without needing to explicitly represent perlocutions. The agents’ belief states are filtered with the observed utterances, resulting in an updated Kripke structure. I then translate my formalization to a practical system that can learn from a small dataset and is able to perform well using very little structural background knowledge in the form of a relational dynamic Bayesian network structure