Focused word spotting in spoken Korean based on fundamental frequency

Kwon, Soonil

doi:10.1587/elex.8.1149

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…To address this issue and advance the functional content market along with related industries, focused research on innovative interfaces and their application to content production must be undertaken. 1,2,3,4 To provide the user with user-oriented intelligent services, a technology that identifies and processes human thoughts, personality traits, and emotions for efficient and accurate recognition of user features is required. User speech signals contain not only linguistic meanings but also elements of the emotions which are the features of the speaker.…”

Section: Introductionmentioning

confidence: 99%

User-Personality Classification Based on the Non-Verbal Cues from Spoken Conversations

Kwon¹,

Choeh²,

Lee³

2013

IJCIS

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Technology that detects user personality based on user speech signals must be researched to enhance the function of interaction between a user and virtual agent that takes place through a speech interface. In this study, personality patterns were automatically classified as either extroverted or introverted. Personality patterns were recognized based on non-verbal cues such as the rate, energy, pitch, and silent intervals of speech with patterns of their change. Through experimentation, a maximum pattern classification accuracy of 86.3% was achieved. Using the same data, another pattern classification test was manually carried out by people to see how well the automatic pattern classification of personal traits performed. The results in the second manual test showed an accuracy of 86.6%. This proves that the automatic pattern classification of personal traits can achieve results comparable to the level of performance accomplished by humans. The Silent Intervals feature of the automatic pattern classification performed admirably while in the second test done by people, pitch was a key factor in producing better accuracy. This information will be useful and applicable in future studies.

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Section: Introductionmentioning

confidence: 99%