• Phonetics and Speech Sciences
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• v.6 no.3
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• pp.57-62
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• 2014

The present study was performed to investigate and discuss the aerodynamic characteristics of whispered and normal speech during reading paragraph tasks. 39 normal females(18-23 yrs.) read 'Autumn' paragraph with whispered and normal phonation. Their readings were recorded and analyzed by 'Running Speech' in Phonatory Aerodynamic System(PAS) instrument. As results, during whispered speech, the total duration was longer and the numbers of inspiration were more frequently shown than normal speech. The Peak expiratory and inspiratory rate were higher in normal speech, but the expiratory and inspiratory volume were higher in whispered speech. By correlation analysis, both whispered and normal speech showed significantly high correlation between total duration and expiratory/inspiratory airflow duration; numbers of inspiration and inspiratory airflow duration; expiratory and inspiratory volume. These results show that whispered speech needs more respiratory effort but shows poorer aerodynamic efficacy during phonation than normal speech.

• 전자공학회논문지 IE
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• v.43 no.2
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• pp.22-29
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• 2006

Recently the use of whispered speech has increased due to mobile phone and the necessity of whispered speech recognition is increasing. So various feature vectors, which are mainly used for speech recognition, are applied to their HMMs, normal speech models, whispered speech models, and integrated models with normal speech and whispered speech so as to find out suitable recognition system for whispered speech. The experimental results of recognition test show that the recognition rate of whispered speech applied to normal speech models is too low to be used in practical applications, but separate whispered speech models recognize whispered speech with the highest rates at least 85%. And also integrated models with normal speech and whispered speech score acceptable recognition rate but more study is needed to increase recognition rate. MFCE and PLCC feature vectors score higher recognition rate when applied to separate whispered speech models, but PLCC is the best when a lied to integrated models with normal speech and whispered speech.