scholarly journals MuBeFE: Multimodal Behavioural Features Extraction Method

2021 ◽  
Vol 27 (3) ◽  
pp. 254-284
Author(s):  
Alessia D'Andrea ◽  
Maria Chiara Caschera ◽  
Fernando Ferri ◽  
Patrizia Grifoni
Keyword(s):  
Extraction Method ◽  
Markov Models ◽  
Hidden Markov ◽  
Evaluation Process ◽  
Communication Style ◽  
Communicative Intention ◽  
The Social ◽  
Social Style ◽  
The Individual ◽  
The Voice

The paper aims to provide a method to analyse and observe the characteristics that distinguish the individual communication style such as the voice intonation, the size and slant used in handwriting and the trait, pressure and dimension used for sketching. These features are referred to as Communication Extensional Features. Observing from the Communication Extensional Features, the user’s behavioural features, such as the communicative intention, the social style and personality traits can be extracted. These behavioural features are referred to as Communication Intentional Features. For the extraction of Communication Intentional Features, a method based on Hidden Markov Models is provided in the paper. The Communication Intentional Features have been extracted at the modal and multimodal level; this represents an important novelty provided by the paper. The accuracy of the method was tested both at modal and multimodal levels. The evaluation process results indicate an accuracy of 93.3% for the Modal layer (handwriting layer) and 95.3% for the Multimodal layer.

Using hidden Markov models to find discrete targets in continuous sociophonetic data

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Daniel Duncan
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Abstract Representation ◽  
Language Variation And Change ◽  
Novel Approach ◽  
Single Target ◽  
The Individual ◽  
Individual Speaker

Abstract Advances in sociophonetic research resulted in features once sorted into discrete bins now being measured continuously. This has implied a shift in what sociolinguists view as the abstract representation of the sociolinguistic variable. When measured discretely, variation is variation in selection: one variant is selected for production, and factors influencing language variation and change are influencing the frequency at which variants are selected. Measured continuously, variation is variation in execution: speakers have a single target for production, which they approximate with varying success. This paper suggests that both approaches can and should be considered in sociophonetic analysis. To that end, I offer the use of hidden Markov models (HMMs) as a novel approach to find speakers’ multiple targets within continuous data. Using the lot vowel among whites in Greater St. Louis as a case study, I compare 2-state and 1-state HMMs constructed at the individual speaker level. Ten of fifty-two speakers’ production is shown to involve the regular use of distinct fronted and backed variants of the vowel. This finding illustrates HMMs’ capacity to allow us to consider variation as both variant selection and execution, making them a useful tool in the analysis of sociophonetic data.

Histogram of Marked Background (HMB) Feature Extraction Method for Arabic Handwriting Recognition

2021 ◽  
pp. 2250015
Author(s):  
Meriem Gagaoua ◽  
Hamza Ghilas ◽  
Abdelkamel Tari ◽  
Mohamed Cheriet
Keyword(s):  
Extraction Method ◽  
Markov Models ◽  
Hidden Markov ◽  
Handwriting Recognition ◽  
Features Extraction ◽  
Complex Nature ◽  
Ibn Sina ◽  
Feature Extraction Method ◽  
Recognition Systems ◽  
Background Pixel

Features extraction is one of the most important steps in handwriting recognition systems. In this paper, we propose a novel features extraction method, which is adapted to the complex nature of Arabic handwriting. The proposed feature called histogram of marked background (HMB) is not considering only ink pixels in a text image, but also uses the background of the image. Each background pixel in the text image was marked according to the repartition of ink pixels in its neighborhood. Feature vectors are extracted by computing histograms from the marked images. Hidden Markov models (HMMs) with Hidden Markov model toolkit (HTK) were used in the recognition process. The experiments were performed on two datasets: IBN SINA database of historical Arabic documents and Isolated Farsi Handwritten Character Database (IFHCDB). The proposed feature in this study produced efficient and promising results for Arabic handwriting recognition, for both isolated characters and for historical documents.

Building Personalized Synthetic Voices for Individuals with Dysarthria using the HTS Toolkit

2010 ◽  
pp. 92-115 ◽  
Author(s):  
Sarah Creer ◽  
Phil Green ◽  
Stuart Cunningham ◽  
Junichi Yamagishi
Keyword(s):  
Statistical Model ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Speech Communication ◽  
Sufficient Information ◽  
Speech Impairment ◽  
New Approach ◽  
Synthetic Voices ◽  
The Voice

For an individual with a speech impairment, it can be necessary for them to use a device to produce synthesized speech to assist their communication. To fully support all functions of human speech communication: communication of information, maintenance of social relationships and displaying identity, the voice must be intelligible and natural-sounding. Ideally, it must also be capable of conveying the speaker’s vocal identity. A new approach based on Hidden Markov models (HMMs) has been proposed as a way of capturing sufficient information about an individual’s speech to enable a personalized speech synthesizer to be developed. This approach adapts a statistical model of speech towards the vocal characteristics of an individual. This chapter describes this approach and how it can be implemented using the HTS toolkit. Results are reported from a study that built personalized synthetic voices for two individuals with dysarthria. An evaluation of the voices by the participants themselves suggests that this technique shows promise for building personalized voices for individuals with progressive dysarthria even when their speech has begun to deteriorate.

scholarly journals Improving Location Prediction by Exploring Spatial-Temporal-Social Ties

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Li Wen ◽  
Xia Shi-xiong ◽  
Liu Feng ◽  
Zhang Lei
Keyword(s):  
Markov Model ◽  
Markov Models ◽  
Social Ties ◽  
Predictive Performance ◽  
Location Prediction ◽  
The Social ◽  
Location Based Social Network ◽  
The Difference ◽  
The Individual ◽  
Time Division

As there is great differences of movement patterns and social correlation between weekdays and weekends, we propose a fallback social-temporal-hierarchic Markov model (FSTHM) to predict individual’s future location. The division of weekdays and weekends is used to decompose the original state of traditional Markov model into two different states and distinguish the difference of the strength of social ties on weekdays and weekends. Except for the time division, the distribution of the visit time for each state is also considered to improve the predictive performance. In addition, in order to best suit the characteristics of Markov model, we introduce the modified cross-sample entropy to quantify the similarities between the individual and his friends. The experiments based on real location-based social network show the FSTHM model gives a 9% improvement over the Markov model and 2% improvement over the social Markov models which use cosine similarity or mutual information to measure the social correlation.

scholarly journals Individual Identification Through Voice Using Mel-Frequency Cepstrum Coefficient (MFCC) and Hidden Markov Models (HMM) Method

2020 ◽  
Vol 7 (1) ◽  
pp. 26
Author(s):  
Dea Sifana Ramadhina ◽  
Rita Magdalena ◽  
Sofia Saidah
Keyword(s):  
Hidden Markov Models ◽  
Speaker Recognition ◽  
Speech Signal ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition System ◽  
Individual Identification ◽  
Acoustic Speech Signal ◽  
Mel Frequency Cepstrum Coefficient ◽  
The Voice

Voice is one of the parameters in the identification process of a person. Through the voice, information will be obtained such as gender, age, and even the identity of the speaker. Speaker recognition is a method to narrow down crimes and frauds committed by voice. So that it will minimize the occurrence of faking one's identity. The Method of Mel Frequency Cepstrum Coefficient (MFCC) can be used in the speech recognition system. The process of feature extraction of speech signal using MFCC will produce acoustic speech signal. The classification, Hidden Markov Models (HMM) is used to match unidentified speaker’s voice with the voices in database. In this research, the system is used to verify the speaker, namely 15 text dependent in Indonesian. On testing the speaker with the same as database, the highest accuracy is 99,16%.

scholarly journals When faced with increasing complexity: The effectiveness of AI assistance for drone design

2021 ◽  
pp. 1-38
Author(s):  
Binyang Song ◽  
Nicolas F Soria Zurita ◽  
Hannah Nolte ◽  
Harshika Singh ◽  
Jonathan Cagan ◽  
...  
Keyword(s):  
Design Process ◽  
Quantitative Methods ◽  
Search Strategy ◽  
Design Research ◽  
Markov Models ◽  
Hidden Markov ◽  
Parameter Design ◽  
Research Platform ◽  
Design Effectiveness ◽  
The Individual

Abstract As Artificial Intelligence (AI) assistance tools become more ubiquitous in engineering design, it becomes increasingly necessary to understand the influence of AI assistance on the design process and design effectiveness. Previous work has shown the advantages of incorporating AI design agents to assist human designers. However, the influence of AI assistance on the behavior of designers during the design process is still unknown. This study examines the differences in participants' design process and effectiveness with and without AI assistance during a complex drone design task using the HyForm design research platform. Data collected from this study is analyzed to assess the design process and effectiveness using quantitative methods, such as Hidden Markov Models and network analysis. The results indicate that AI assistance is most beneficial when addressing moderately complex objectives but exhibits a reduced advantage in addressing highly complex objectives. During the design process, the individual designers working with AI assistance employ a relatively explorative search strategy, while the individual designers working without AI assistance devote more effort to parameter design.

scholarly journals Regime-Switching Factor Investing with Hidden Markov Models

2020 ◽  
Vol 13 (12) ◽  
pp. 311
Author(s):  
Matthew Wang ◽  
Yi-Hong Lin ◽  
Ilya Mikhelson
Keyword(s):  
Regime Switching ◽  
Historical Data ◽  
Markov Models ◽  
Factor Model ◽  
Hidden Markov ◽  
Investment Strategy ◽  
Factor Models ◽  
Individual Factor ◽  
Market Regimes ◽  
The Individual

This study uses the hidden Markov model (HMM) to identify different market regimes in the US stock market and proposes an investment strategy that switches factor investment models depending on the current detected regime. We first backtested an array of different factor models over a roughly 10.5 year period from January 2007 to September 2017, then we trained the HMM on S&P 500 ETF historical data to identify market regimes of that period. By analyzing the relationship between factor model returns and different market regimes, we are able to establish the basis of our regime-switching investing model. We then back-tested our model on out-of-sample historical data from September 2017 to April 2020 and found that it both delivers higher absolute returns and performs better than each of the individual factor models according to traditional portfolio benchmarking metrics.

scholarly journals Predicting Social Unrest Events with Hidden Markov Models Using GDELT

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Fengcai Qiao ◽  
Pei Li ◽  
Xin Zhang ◽  
Zhaoyun Ding ◽  
Jiajun Cheng ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Classification Problem ◽  
Sequence Classification ◽  
Social Unrest ◽  
Event Prediction ◽  
The Social ◽  
Logistic Regression Method ◽  
Burst Patterns

Proactive handling of social unrest events which are common happenings in both democracies and authoritarian regimes requires that the risk of upcoming social unrest event is continuously assessed. Most existing approaches comparatively pay little attention to considering the event development stages. In this paper, we use autocoded events dataset GDELT (Global Data on Events, Location, and Tone) to build a Hidden Markov Models (HMMs) based framework to predict indicators associated with country instability. The framework utilizes the temporal burst patterns in GDELT event streams to uncover the underlying event development mechanics and formulates the social unrest event prediction as a sequence classification problem based on Bayes decision. Extensive experiments with data from five countries in Southeast Asia demonstrate the effectiveness of this framework, which outperforms the logistic regression method by 7% to 27% and the baseline method 34% to 62% for various countries.

Liberté d'opinion et limite normative dans une représentation sociale: le développement de l'intelligence 1Ce texte reprend l'essentiel d'un exposé présenté en juin 1998 à la Faculté de Psychologie et des Sciences de l'Education de Genève, à l'occasion de la collation du Doctorat Honoris Causa de l'Université de Genève à l'auteur.

1999 ◽  
Vol 58 (3) ◽  
pp. 201-206 ◽  
Author(s):  
Claude Flament
Keyword(s):  
Social Representations ◽  
The Social ◽  
The Individual

This paper is concerned by a possible articulation between the diversity of individual opinions and the existence of consensus in social representations. It postulates the existence of consensual normative boundaries framing the individual opinions. A study by questionnaire about the social representations of the development of intelligence gives support to this notion.

A Moment to Come Together: Personal Reflections on Trayvon Martin

2013 ◽  
Vol 5 (1) ◽  
pp. 131-137
Author(s):  
Roxanne Christensen ◽  
LaSonia Barlow ◽  
Demetrius E. Ford
Keyword(s):  
Civil Rights ◽  
Doctoral Students ◽  
Professional Psychology ◽  
Direct Effects ◽  
The Social ◽  
To Come ◽  
Professional Responses ◽  
The Individual ◽  
Personal Reflections ◽  
Unique Individual

Three personal reflections provided by doctoral students of the Michigan School of Professional Psychology (Farmington Hills, Michigan) address identification of individual perspectives on the tragic events surrounding Trayvon Martin’s death. The historical ramifications of a culture-in-context and the way civil rights, racism, and community traumatization play a role in the social construction of criminals are explored. A justice orientation is applied to both the community and the individual via internal reflection about the unique individual and collective roles social justice plays in the outcome of these events. Finally, the personal and professional responses of a practitioner who is also a mother of minority young men brings to light the need to educate against stereotypes, assist a community to heal, and simultaneously manage the direct effects of such events on youth in society. In all three essays, common themes of community and growth are addressed from varying viewpoints. As worlds collided, a historical division has given rise to a present unity geared toward breaking the cycle of violence and trauma. The authors plead that if there is no other service in the name of this tragedy, let it at least contribute to the actualization of a society toward growth and healing.

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