This work is aimed at exploiting second-order circular suprasegmental hidden Markov models (CSPHMM2s) as classifiers to enhance talking condition recognition in stressful and emotional talking environments (completely two separate environments). The stressful talking environment that has been used in this work uses speech under simulated and actual stress database, while the emotional talking environment uses emotional prosody speech and transcripts database. The achieved results of this work using mel-frequency cepstral coefficients demonstrate that CSPHMM2s outperform each of hidden Markov models, second-order circular hidden Markov models, and suprasegmental hidden Markov models in enhancing talking condition recognition in the stressful and emotional talking environments. The results also show that the performance of talking condition recognition in stressful talking environments leads that in emotional talking environments by 3.67 % based on CSPHMM2s. Our results obtained in subjective evaluation by human judges fall within 2.14 and 3.08 % of those obtained, respectively, in stressful and emotional talking environments based on CSPHMM2s.Keywords Emotional talking environments · Hidden Markov models · Second-order circular hidden Markov models · Second-order circular suprasegmental hidden Markov models · Stressful talking environments · Suprasegmental hidden Markov models

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We propose a novel generic trust management framework for crowdsourced IoT services. The framework exploits a multi-perspective trust model that captures the inherent characteristics of crowdsourced IoT services. Each perspective is defined by a set of attributes that contribute to the perspective's influence on trust. The attributes are fed into a machine-learning-based algorithm to generate a trust model for crowdsourced services in IoT environments. We demonstrate the effectiveness of our approach by conducting experiments on real-world datasets.

This work is devoted to capturing Emirati-accented speech database (Arabic United Arab Emirates database) in each of neutral and shouted talking environments in order to study and enhance text-independent Emirati-accented "speaker identification performance in shouted environment" based on each of "First-Order Circular Suprasegmental Hidden Markov Models (CSPHMM1s), Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s), and Third-Order Circular Suprasegmental Hidden Markov Models (CSPHMM3s)" as classifiers.In this research, our database was collected from fifty Emirati native speakers (twenty five per gender) uttering eight common Emirati sentences in each of neutral and shouted talking environments. The extracted features of our collected database are called "Mel-Frequency Cepstral Coefficients (MFCCs)". Our results show that average Emirati-accented speaker identification performance in neutral environment is 94.0%, 95.2%, and 95.9% based on CSPHMM1s, CSPHMM2s, and CSPHMM3s, respectively. On the other hand, the average performance in shouted environment is 51.3%, 55.5%, and 59.3% based, respectively, on "CSPHMM1s, CSPHMM2s, and CSPHMM3s". The achieved "average speaker identification performance in shouted environment based on CSPHMM3s" is very similar to that obtained in "subjective assessment by human listeners". 2