Speech-driven talking face using embedded confusable system for real time mobile multimedia

Shih, Po-Yi; Paul, Anand; Wang, Jhing-Fa; Chen, Yi‐Hung

doi:10.1007/s11042-013-1609-3

Cited by 4 publications

(4 citation statements)

References 24 publications

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“…Finally on using simulation results which show that the end-to-end delay is minimized in cooperative communication according to the vehicle priority. There are lots of challenges ahead when V2V environment meets up M2M with various soft computing systems [32,[43][44][45], in this ear billions f ubiquitous device are connected together to form a huge mesh of big data [46,47] apart from that now a days voice controlled vehicles are facing real-time implementation challenges. …”

Section: Resultsmentioning

confidence: 99%

Cooperative Intelligence of Vehicles for Intelligent Transportation Systems (ITS)

Daniel

Paul

Ahmad

et al. 2015

Wireless Pers Commun

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The aim of Intelligent Transportation Systems (ITS) is to automate the interactions among vehicles and infrastructure to accomplish high levels of safety measures, comfort, and competence in vehicular communication. To utilize the future trends of increasing traffic safety and efficiency in ITS, integrating vehicles and infrastructures with the cooperative vehicular technique will be the feasible solution. In order to demonstrate the importance of cooperative communication in vehicular networks, a spectral efficient architecture has been proposed for cooperative centralized and distributed spectrum sensing in vehicular networks. We discuss the possibilities of Cognitive Radio in the cooperative vehicular environment. In order to exhibit cooperative vehicular networks, hardware modules are designed for a vehicle to vehicle, vehicle to infrastructure and infrastructure to infrastructure communications. Furthermore, quantitative analysis is made in order to calculate the energy optimization, connectivity failure probability and traffic management in cooperative vehicular networks. In addition, we test the results of the cooperative vehicular network by simulating it in NS2. In this respect, we have considered three different cases, Emergency vehicles, VIP vehicles, and normal vehicles. It is inferred from the results that end-to-end delay for emergency vehicles in the cooperative environment is considerably less as compared to VIP and normal vehicles.

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Section: Resultsmentioning

confidence: 99%

Cooperative Intelligence of Vehicles for Intelligent Transportation Systems (ITS)

Daniel

Paul

Ahmad

et al. 2015

Wireless Pers Commun

Self Cite

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“…This will be a problem when the system is implemented for real-time animation. Map performance improvements were also made in the phoneme confusion section [5]. Whereas another research uses linguistics to create P2V maps and uses surveys for validation [6].…”

Section: Introductionmentioning

confidence: 99%

Improving Phoneme to Viseme Mapping for Indonesian Language

Rachman

Hidayat

Nugroho

2020

IJITEE

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The lip synchronization technology of animation can run automatically through the phoneme-to-viseme map. Since the complexity of facial muscles causes the shape of the mouth to vary greatly, phoneme-to-viseme mapping always has challenging problems. One of them is the allophone vowel problem. The resemblance makes many researchers clustering them into one class. This paper discusses the certainty of allophone vowels as a variable of the phoneme-to-viseme map. Vowel allophones pre-processing as a proposed method is carried out through formant frequency feature extraction methods and then compared by t-test to find out the significance of the difference. The results of pre-processing are then used to reference the initial data when building phoneme-to-viseme maps. This research was conducted on maps and allophones of the Indonesian language. Maps that have been built are then compared with other maps using the HMM method in the value of word correctness and accuracy. The results show that viseme mapping preceded by allophonic pre-processing makes map performance more accurate when compared to other maps.

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“…This special issue covers a wide range of topics in human-machine interaction, including text-or speech-driven facial animation [11,23,28], emphatic speech synthesis [20], head and facial gesture synthesis [10], human pose estimation [24], crowd counting [6], person identification [25] and facial expression recognition [8].…”

mentioning

confidence: 99%

“…In the mobile computing age, talking avatars, or virtual assistants, are playing an increasingly important role in serving a natural user interface due to the physical limits of smartphones and special features of mobile platforms. In [23], Shih et al aim to synthesize a real-time speech-driven talking face for mobile multimedia. A lifelike talking avatar requires not only natural speech articulation, but also expressive head motions, emotional facial expressions and other meaningful facial gestures [15].…”

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confidence: 99%

Multimodal joint information processing in human machine interaction: recent advances

Xie

Deng

Cox

2013

Multimed Tools Appl

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Interaction between humans is a multimodal process in nature, which integrates the different modalities of vision, hearing, gesture and touch. It is not surprising then that conventional uni-modal human-machine interactions lag in performance, robustness and naturalness when compared with human-human interactions. Recently, there has been increasing research interest in jointly processing information in multiple modalities and mimicking human-human multimodal interactions [2,4,5,9,13,14,16,18,19,21,22]. For example, human speech production and perception are bimodal in nature: visual cues have a broad influence on perceived auditory stimuli [17]. The latest research in speech processing has shown that integration of both auditory and visual information, i.e., acoustic speech combined with facial and lip motions, achieves significant performance improvement in tasks such as speech recognition [3] and emotion recognition [16]. However, information from difference modalities can be difficult to integrate: the representations of different signals are heterogeneous and the signals are often asynchronous and loosely-coupled. Therefore, finding more effective ways to integrate and jointly process information from different modalities is essential for the success of such multimodal human-machine interactive systems. Meanwhile, multimodal applications are becoming increasingly important, especially for mobile computing and digital entertainment scenarios, such as natural user interfaces (NUI) on smartphones and motion sensing gaming.This special issue aims to bring together work by researchers and technologists engaged in the development of multimodal technologies for information processing, emerging multimedia applications and user-centric human computer interaction. We received more than L. Xie ( )

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Speech-driven talking face using embedded confusable system for real time mobile multimedia

Cited by 4 publications

References 24 publications

Cooperative Intelligence of Vehicles for Intelligent Transportation Systems (ITS)

Cooperative Intelligence of Vehicles for Intelligent Transportation Systems (ITS)

Improving Phoneme to Viseme Mapping for Indonesian Language

Multimodal joint information processing in human machine interaction: recent advances

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