Social perception of male and female computer synthesized speech

Mullennix, John W.; Stern, Steven; Wilson, Stephen J.; Dyson, Corrie Lynn

doi:10.1016/s0747-5632(02)00081-x

Cited by 54 publications

(60 citation statements)

References 12 publications

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“…In regards to those sentences, speech files were generated by twoT TS systems (male voices)a nd recorded from one natural speaker (male). The natural speech sample wasr ecorded in an anechoic environment; he wasn ot ap rofessional speaker.T he decision to use am ale voice wasi nfluenced by aprevious study published in [29]. These tests proved that the message produced by the male synthetic voice was rated as more favourable (e.g.…”

Section: Speech Materialsmentioning

confidence: 98%

Predicting the Quality of Synthesized and Natural Speech Impaired by Packet Loss and Coding Using PESQ and P.563 Models

Počta¹,

Holub²

2011

Acta Acustica united with Acustica

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SummaryThis paper investigates the impact of independent and dependent losses and coding on speech quality predictions provided by PESQ (also known as ITU-T P. 862)a nd P. 563 models, when both naturally-produced and synthesized speech are used. Twos ynthesized speech samples generated with twod i ff erent Text-to-Speech systems and one naturally-produced sample are investigated. In addition, we assess the variability of PESQ'sand P. 563's predictions with respect to the type of speech used (naturally-produced or synthesized)a nd loss conditions as well as their accuracy, by comparing the predictions with subjective assessments. The results showthat there is no difference between the impact of packet loss on naturally-produced speech and synthesized speech. On the other hand, the impact of coding is different for the twotypes of stimuli. In addition, synthesized speech seems to be insensitive to degradations provided by most of the codecs investigated here. The reasons for those findings are particularly discussed. Finally,itisconcluded that both models are capable of predicting the quality of transmitted synthesized speech under the investigated conditions to acertain degree. As expected, PESQ achievesthe best performance overalmost all of the investigated conditions.

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Section: Speech Materialsmentioning

confidence: 98%

Predicting the Quality of Synthesized and Natural Speech Impaired by Packet Loss and Coding Using PESQ and P.563 Models

Počta¹,

Holub²

2011

Acta Acustica united with Acustica

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“…Virtual characters, embodied agents, and speech interfaces have been studied in numerous experiments to understand the impact digital interfaces have on persuasion. For instance, Zanbaka et al studied the role of gender and realism in the persuasiveness of a virtual cat agent [39]; Schulman and Bickmore studied the effect of fidelity to human conversation of an embodied agent to engage a user in a persuasive dialogue [35]; Stern et al studied the persuasiveness of synthetic speech versus human speech [37]; and Mullenix et al studied the effect of gender in voice-based interfaces on their degree of persuasion [24].…”

Section: Persuasion In User Interfacesmentioning

confidence: 99%

“…Attitude change research aims at recording changes in attitude as an effect of exposure to a persuasive message. Following the ELM [33] and numerous studies that use ELM as the main theoretical framework of reference [39,35,37,24,21], we structured our experiment around the following steps: (1) Topic description: A brief and neutral description of the topic under discussion is provided to the participant; (2) Pre-treatment attitude: Attitude towards the topic under discussion is recorded before the participant is exposed to the persuasive message; (3) Persuasive message: The participant is presented with the persuasive message; (4) Post-treatment attitude: Attitude towards the topic under discussion is recorded again after the participant is exposed to the persuasive message. Attitude change is measured as the difference between the post-treatment and pre-treatment attitude.…”

Section: Topicmentioning

confidence: 99%

The Persuasive Power of Data Visualization

Pandey

Manivannan

Nov

et al. 2014

IEEE Trans. Visual. Comput. Graphics

162

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Abstract-Data visualization has been used extensively to inform users. However, little research has been done to examine the effects of data visualization in influencing users or in making a message more persuasive. In this study, we present experimental research to fill this gap and present an evidence-based analysis of persuasive visualization. We built on persuasion research from psychology and user interfaces literature in order to explore the persuasive effects of visualization. In this experimental study we define the circumstances under which data visualization can make a message more persuasive, propose hypotheses, and perform quantitative and qualitative analyses on studies conducted to test these hypotheses. We compare visual treatments with data presented through barcharts and linecharts on the one hand, treatments with data presented through tables on the other, and then evaluate their persuasiveness. The findings represent a first step in exploring the effectiveness of persuasive visualization.

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“…Furthermore, a comparative analysis of subjective dimensions between male and female voices, using ANOVA, indicated a significant difference for male listeners, where the male listeners rated QoE-MOS and Valence for male voices higher than female voices with F (1, 258) = 15.72; p ≤ 0.01 and F (1, 258) = 6.49; p ≤ 0.05, respectively. Previous research has found similar preference of male voices over female voices, for male listeners [57], and, male and female listeners [58].…”

Section: Subjective Data Evaluationmentioning

confidence: 58%

Using affective brain-computer interfaces to characterize human influential factors for speech quality-of-experience perception modelling

Gupta

Laghari

Banville

et al. 2016

Hum. Cent. Comput. Inf. Sci.

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As new speech technologies emerge, telecommunication service providers have to provide superior user experience in order to remain competitive. To this end, quality-of-experience (QoE) perception modelling and measurement has become a key priority. QoE models rely on three influence factors: technological, contextual and human. Existing solutions have typically relied on the former two and human influence factors (HIFs) have been mostly neglected due to difficulty in measuring them. In this paper, we show that measuring human affective states is important for QoE measurement and propose the use of affective brain-computer interfaces (aBCIs) for objective measurement of perceived QoE for two emerging speech technologies, namely far-field hands-free communications and text-to-speech systems. When incorporating subjectively-derived HIFs into the QoE model, gains of up to 26.3 % could be found relative to utilizing only technological factors. When utilizing HIFs derived from an electroencephalography (EEG) based aBCI, in turn, gains of up to 14.5 % were observed. These findings show the importance of using aBCIs in QoE measurement and also highlight that further improvement may be warranted once improved affective state correlates are found from EEGs and/or other neurophysiological modalities.

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Social perception of male and female computer synthesized speech

Cited by 54 publications

References 12 publications

Predicting the Quality of Synthesized and Natural Speech Impaired by Packet Loss and Coding Using PESQ and P.563 Models

Predicting the Quality of Synthesized and Natural Speech Impaired by Packet Loss and Coding Using PESQ and P.563 Models

The Persuasive Power of Data Visualization

Using affective brain-computer interfaces to characterize human influential factors for speech quality-of-experience perception modelling

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