Speaker Recognition for Robotic Control via an IoT Device

Kozhirbayev, Zhanibek; Erol, Berat A.; Sharipbay, Altynbek; Jamshidi, Mo

doi:10.23919/wac.2018.8430295

Cited by 15 publications

(8 citation statements)

References 24 publications

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“…The main point is to facilitate collection of a similar database in social robot environments, such as robots working with speech impairment person or robots working with people with developmental disability as well as robots working in assisted living and hospitals [ 35 , 63 , 64 ]. Additionally, the choice of the number of speakers was selected to emulate social human–robot interaction tasks where social robots interact with a limited number of persons [ 65 ]. The speech samples were divided equally into three sets; namely training, development, and testing sets.…”

Section: Resultsmentioning

confidence: 99%

A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation

Al-Qaderi

Lahamer

Rad

2021

Sensors

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We present a new architecture to address the challenges of speaker identification that arise in interaction of humans with social robots. Though deep learning systems have led to impressive performance in many speech applications, limited speech data at training stage and short utterances with background noise at test stage present challenges and are still open problems as no optimum solution has been reported to date. The proposed design employs a generative model namely the Gaussian mixture model (GMM) and a discriminative model—support vector machine (SVM) classifiers as well as prosodic features and short-term spectral features to concurrently classify a speaker’s gender and his/her identity. The proposed architecture works in a semi-sequential manner consisting of two stages: the first classifier exploits the prosodic features to determine the speaker’s gender which in turn is used with the short-term spectral features as inputs to the second classifier system in order to identify the speaker. The second classifier system employs two types of short-term spectral features; namely mel-frequency cepstral coefficients (MFCC) and gammatone frequency cepstral coefficients (GFCC) as well as gender information as inputs to two different classifiers (GMM and GMM supervector-based SVM) which in total leads to construction of four classifiers. The outputs from the second stage classifiers; namely GMM-MFCC maximum likelihood classifier (MLC), GMM-GFCC MLC, GMM-MFCC supervector SVM, and GMM-GFCC supervector SVM are fused at score level by the weighted Borda count approach. The weight factors are computed on the fly via Mamdani fuzzy inference system that its inputs are the signal to noise ratio and the length of utterance. Experimental evaluations suggest that the proposed architecture and the fusion framework are promising and can improve the recognition performance of the system in challenging environments where the signal-to-noise ratio is low, and the length of utterance is short; such scenarios often arise in social robot interactions with humans.

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Section: Resultsmentioning

confidence: 99%

A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation

Al-Qaderi

Lahamer

Rad

2021

Sensors

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“…The GMM modeling of acoustic features is obtained with the sum of the m-component densities. Eqn (1) shows the step of acoustic modeling. (1) [24].…”

Section: Fig 3 Gmm Modelling Of Speech Datamentioning

confidence: 99%

“…Speech communication-based IoT devices [1] are one of the significant societal applications. Speaker clustering [2] is an emerging problem in many real-time applications.…”

Section: Introductionmentioning

confidence: 99%

Sampling Based Fuzzy Speech Clustering Systems for Faster Communication with Virtual Robotics Towards Social Applications

Prasad¹,

Narasimhulu²,

KUMAR³

et al. 2023

Preprint

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For social applications, virtual robotics technologies require the Internet of Things (IoT) and cloud services. IoT based speaker identification is required for virtual social robotics to operate in the most secure manner possible. Moreover, humans communicate using only natural speech to virtual robotics through IoT devices; Virtual robotics are the most emerging and which are required developing the speech-based identification models in the virtual cloud platforms. State-of-the-art of speech models defines the composite technique with the combination of k-means clustering and Gaussian mixture model (GMM) for speaker identification problems. Every speaker has their own annotation, acoustic, and utterance. The Gaussian mixture model (GMM) describes the modelling of features in the speech clustering system for speaker identification applications. These traditional systems are expensive for speech clustering of large datasets when considering the time and space parameters. Sampling based fuzzy-spherical k-means and sampling based fuzzy-mini-batch k-means(mbkm) are proposed to overcome the problem of scalability issue in the implementation of speech based virtual cloud based robotic systems. For demonstrating the effectiveness of the proposed fuzzy based speaker models, extensive experimental research is conducted using a variety of benchmark speech datasets.

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“…Nevertheless, this type of interface can not replace the classical GUIs but it can intensify them by providing, in some cases, a more efficient way of interaction. The authors in [114] developed a technique to train a…”

Section: Human Centric Perceptionmentioning

confidence: 99%

Empowering cyberphysical systems of systems with intelligence

Nousias¹,

Piperigkos²,

Arvanitis³

et al. 2021

Preprint

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Cyber Physical Systems have been going into a transition phase from individual systems to a collecttives of systems that collaborate in order to achieve a highly complex cause, realizing a system of systems approach. The automotive domain has been making a transition to the system of system approach aiming to provide a series of emergent functionality like traffic management, collaborative car fleet management or large-scale automotive adaptation to physical environment thus providing significant environmental benefits (e.g air pollution reduction) and achieving significant societal impact. Similarly, large infrastructure domains, are evolving into global, highly integrated cyber-physical systems of systems covering all parts of the value chain. In practice, there are significant challenges in CPSoS applicability and usability to be addressed, i.e. even a small CPSoS such as a car consists several subsystems Decentralization of CPSoS appoints tasks to individual CPSs within the System of Systems. CPSoSs are heterogenous systems. They comprise of various, autonomous, CPSs, each one of them having unique performance capabilities, criticality level, priorities and pursued goals. all CPSs must also harmonically pursue systembased achievements and collaborate in order to make system-ofsystem based decisions and implement the CPSoS functionality. This survey will provide a comprehensive review on current best practices in connected cyberphysical systems. The basis of our investigation is a dual layer architecture encompassing a perception layer and a behavioral layer. Perception algorithms with respect to scene understanding (object detection and tracking, pose estimation), localization mapping and path planning are thoroughly investigated. Behavioural part focuses on decision making and human in the loop control.

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Speaker Recognition for Robotic Control via an IoT Device

Cited by 15 publications

References 24 publications

A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation

A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation

Sampling Based Fuzzy Speech Clustering Systems for Faster Communication with Virtual Robotics Towards Social Applications

Empowering cyberphysical systems of systems with intelligence

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