Acoustic features correlated to perceived urgency in evacuation announcements

Kobayashi, Minoru; Hamada, Yusuke; Akagi, Masato

doi:10.1016/j.specom.2022.03.001

Cited by 5 publications

(8 citation statements)

References 30 publications

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“…Our findings suggest that the shape of the frequency spectrum in the middle and higher ranges is related to the sense of urgency perception [19]. Therefore, we investigated why there is a difference in urgency perception between the original and noise-vocoded stimuli by utilizing sharpness [20], which is a common sound-quality metric.…”

Section: Considerationmentioning

confidence: 99%

“…We utilized the recorded speech data from actual evacuation announcements in our experiments. These were recorded when a tsunami (tidal wave) was predicted after an earthquake off the coast of Fukushima, Japan on 22 November 2016 [16]. The announcements were spoken by multiple professional announcers with various speaking styles on different TV channels.…”

Section: Speech Data In Evacuation Announcementsmentioning

confidence: 99%

“…The announcements were spoken by multiple professional announcers with various speaking styles on different TV channels. We selected one real speech dataset (the same speaker, the same sentence) by a professional male speaker (the same used by Kobayashi & Akagi [16]) as the original stimuli, which was/i/ma/su/gu/ni/ge/te/ku/da/sa/i/("Please evacuate now" in English). This announcement was made at four different levels of urgency ("A," "B," "C," and "D", corresponding to noise-vocoded stimuli labeled "a," "b," "c," and "d") to determine how the urgency was perceived for each.…”

Section: Speech Data In Evacuation Announcementsmentioning

confidence: 99%

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Contributions of Temporal Modulation Cues in Temporal Amplitude Envelope of Speech to Urgency Perception

et al. 2023

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We previously investigated the perception of noise-vocoded speech to determine whether the temporal amplitude envelope (TAE) of speech plays an important role in the perception of linguistic information as well as non-linguistic information. However, it remains unclear if these TAEs also play a role in the urgency perception of non-linguistic information. In this paper, we comprehensively investigated whether the TAE of speech contributes to urgency perception. To this end, we compared noise-vocoded stimuli containing TAEs identical to those of original speech with those containing TAEs controlled by low-pass or high-pass filtering. We derived degrees of urgency from a paired comparison of the results and then used them as a basis to clarify the relationship between the temporal modulation components in TAEs of speech and urgency perception. Our findings revealed that (1) the perceived degrees of urgency of noise-vocoded stimuli are similar to those of the original, (2) significant cues for urgency perception are temporal modulation components of the noise-vocoded stimuli higher than the modulation frequency of 6 Hz, (3) additional significant cues for urgency perception are temporal modulation components of the noise-vocoded stimuli lower than the modulation frequency of 8 Hz, and (4) the TAE of the time-reversal speech is not likely to contain important cues for the perception of urgency. We therefore conclude that temporal modulation cues in the TAE of speech are a significant component in the perception of urgency.

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Section: Considerationmentioning

confidence: 99%

Section: Speech Data In Evacuation Announcementsmentioning

confidence: 99%

Section: Speech Data In Evacuation Announcementsmentioning

confidence: 99%

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Contributions of Temporal Modulation Cues in Temporal Amplitude Envelope of Speech to Urgency Perception

et al. 2023

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“…Method. Low-level features and deep-level features are the two primary divisions of read aloud speech characteristics [19]. Different features have different…”

Section: Design Of Dl-based Se Feature Extractionmentioning

confidence: 99%

Speech Emotion Analysis of Short English Readings based on the CAM-SPAT Model

2023

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With the development of technology, voice sentiment analysis has also undergone rapid development, and its application fields are constantly expanding. Multimodal models have become a key focus of researchers due to their ability to better predict emotions. In order to help English learners improve their oral English proficiency, a deep learning based emotional analysis model for English short text reading is proposed, and this model is used to analyze emotions in English reading. Additionally, a cross-modal attention mechanism based on a prediction-assisted task was developed to identify emotions in English reading aloud in state and a two-layer attention-based bi-directional long- and short-term memory network was created to classify emotions in English reading aloud. The results of the research revealed that the classification model’s mean F1 value was 98.54%, the detection model’s mean F1 value was 85.13%, and the speech emotion analysis model’s mean F1 value was 73.55, which was not significantly different from the mean of the professionals’ ratings. The significance of the study lies in providing English learners with a method and pathway to improve their oral English proficiency.

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“…Heidän onkin todettu olevan herkkiä tunnistamaan ilmoittajan tunnetiloja, kuten pelkoa, vihaa ja surua, minkä lisäksi näistä tunnetiloista tehtyjen havaintojen on todettu vaikuttavan heidän arvioihinsa ilmoituksen kiireellisyysluokituksesta (Svensson & Pesämaa, 2018). Perustaajuuden (F0) ja voimakkuuden on todettu olevan merkityksellisiä tunnetilojen erottamisessa hätätilanteissa (Vidrascu & Devillers, 2005) ja puheen jännittyneisyydellä puolestaan näyttäisi olevan merkitys kiireellisyyden arvioinnissa (Kobayashi, Hamada & Akagi, 2022). Hätäkeskuspäivystäjien uskomuksia tai valheentunnistustarkkuutta ei tiedettävästi ole aiemmin tutkittu, vaikka perättömien hätäilmoitusten tunnistaminen on tärkeää esimerkiksi viranomaisresurssien turhan sitouttamisen välttämiseksi.…”

Section: Johdantounclassified

Suomea äidinkielenään puhuvien viranomaisten ja maallikoiden käsitykset valheen paljastavista merkeistä

Väisänen

2023

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Kansainvälisen stereotyyppisen mielikuvan mukaan valheen voi tunnistaa katseen välttelystä, hermostuneisuudesta ja kehon liikkeistä. Ihmiset uskovat tunnistavansa valheen erityisesti nonverbaaleista merkeistä, vaikka verbaalisten merkkien on todettu olevan luotettavampia valheentunnistuksessa. Viranomaistahoilla on myös todettu olevan stereotyyppisiä uskomuksia valheen merkeistä. Uskomuksissa on kuitenkin havaittu kulttuurisia variaatioita. Tutkimuksessa selvitettiin mistä verbaaleista ja nonverbaaleista merkeistä suomalaiset viranomaiset ja maallikot uskovat tunnistavansa valheen. Lisäksi selvitettiin mistä suomalaiset uskovat jäävänsä itse kiinni valehdellessaan ja miten nämä uskomukset vertautuvat kansainvälisiin tutkimustuloksiin. Verkkokyselylomakkeelle kerättiin vastauksia suomea äidinkielenään puhuvilta poliiseilta, hätäkeskuspäivystäjiltä ja maallikoilta (N = 154). Suomalaiset uskoivat kansainvälisen stereotypian mukaisesti tunnistavansa valheen katseen välttelystä ja hermostuneisuudesta. Myös puheen ristiriitaisuuksien, korjailujen ja välttelevyyden uskottiin olevan merkkejä valehtelusta. Viranomaiset uskoivat maallikoiden tapaan tunnistavansa valheen katseen välttelystä, ja aiempi tietämys valheentunnistamisesta ei vaikuttanut uskomuksiin. Suomalaisilla viranomaisilla oli samantapaisia ja virheellisiä uskomuksia valheen paljastavista merkeistä kuin maallikoilla. Suomalaisten käsitykset eivät juurikaan poikkea kansainvälisistä stereotypioista.

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Acoustic features correlated to perceived urgency in evacuation announcements

Cited by 5 publications

References 30 publications

Contributions of Temporal Modulation Cues in Temporal Amplitude Envelope of Speech to Urgency Perception

Contributions of Temporal Modulation Cues in Temporal Amplitude Envelope of Speech to Urgency Perception

Speech Emotion Analysis of Short English Readings based on the CAM-SPAT Model

Suomea äidinkielenään puhuvien viranomaisten ja maallikoiden käsitykset valheen paljastavista merkeistä

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