Recent Trends in Deep Learning Based Personality Detection

Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection

Hidden Markov Models for Visual Speech Synthesis in Limited Data

This work presents a new approach for estimating control points (facial locations that control movement) to allow the artificial generation of video with apparent mouth movement (visual speech) time-synced with recorded audio. First, Hidden Markov Models (HMMs) are estimated for each visual speech category (viseme) present in stored video data, where a category is defined as the mouth movement corresponding to a given sound and where the visemes are further categorized as trisemes (a viseme in the context of previous and following visemes). Next, a decision tree is used to cluster and relate states in the HMMs that are similar in a contextual and statistical sense. The tree is also used to estimate HMMs that generate sequences of visual speech control points for trisemes not occurring in the stored data. An experiment is described that evaluates the effect of several algorithm variables, and a statistical analysis is presented that establishes appropriate levels for each variable by minimizing the error between the desired and estimated control points. The analysis indicates that the error is lowest when the process is conducted with three-state left-to right no skip HMMs trained using short-duration dynamic features, a high log-likelihood threshold, and a low outlier threshold. Also, comparisons of mouth shapes generated from the artificial control points and the true control points (estimated from video not used to train the HMMs) indicate that the process provides accurate estimates for most trisemes tested in this work. The research presented here thus establishes a useful method for synthesizing realistic audio-synchronized video facial features

Multimodal Based Audio-Visual Speech Recognition for Hard-of-Hearing: State of the Art Techniques and Challenges

Multimodal Integration (MI) is the study of merging the knowledge acquired by the nervous system using sensory modalities such as speech, vision, touch, and gesture. The applications of MI expand over the areas of Audio-Visual Speech Recognition (AVSR), Sign Language Recognition (SLR), Emotion Recognition (ER), Bio Metrics Applications (BMA), Affect Recognition (AR), Multimedia Retrieval (MR), etc. The fusion of modalities such as hand gestures- facial, lip- hand position, etc., are mainly used sensory modalities for the development of hearing-impaired multimodal systems. This paper encapsulates an overview of multimodal systems available within literature towards hearing impaired studies. This paper also discusses some of the studies related to hearing-impaired acoustic analysis. It is observed that very less algorithms have been developed for hearing impaired AVSR as compared to normal hearing. Thus, the study of audio-visual based speech recognition systems for the hearing impaired is highly demanded for the people who are trying to communicate with natively speaking languages.  This paper also highlights the state-of-the-art techniques in AVSR and the challenges faced by the researchers for the development of AVSR systems

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 314)

This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1988

Perception arising from simultaneous signals through two sensory channels

Imperial Users onl

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes