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dc.contributor.authorArias Vergara, Tomás
dc.contributor.authorArgüello Vélez, Patricia
dc.contributor.authorVásquez Correa, Juan Camilo
dc.contributor.authorNöth, Elmar
dc.contributor.authorSchuster, Maria Elke
dc.contributor.authorGonzález Rátiva, Marí­a Claudia
dc.contributor.authorOrozco Arroyave, Juan Rafael
dc.date.accessioned2020-10-23T22:24:25Z
dc.date.available2020-10-23T22:24:25Z
dc.date.issued2020-05-27
dc.identifier.issn1051-2004
dc.identifier.urihttp://repository.usc.edu.co/handle/20.500.12421/4532
dc.description.abstractVoice Onset Time (VOT) has been used by researchers as an acoustic measure in order to gain some understanding about the impact of different motor speech disorders in speech production. However, VOT values are usually obtained manually, which is expensive and time consuming. In this paper we proposed a method for the automatic detection of VOT based on pre-trained Recurrent Neural Networks with Gated Recurrent Units (GRUs). Speech recordings from 50 Spanish native speakers from Colombia (25 male) are considered for the experiments. The recordings include the utterance of the diadochokinesis task /pa-ta-ka/ which is typically used for the evaluation of motor speech disorders like those caused due to Parkinson's disease. Additionally, the diadochokinesis task allows us to train a system to detect the VOT of voiceless plosive sounds in intermediate positions. Acoustic analysis is performed by extracting different temporal and spectral features from the recordings. According to the results, it is possible to detect the VOT with F1-score values of 0.66 for Image 1, 0.75 for Image 2, and 0.78 for Image 3 when the predicted values are compared with respect to the manual VOT labels.en_US
dc.language.isoenen_US
dc.publisherElsevier Inc.en_US
dc.subjectDiadochokinesisen_US
dc.subjectRecurrent neural networken_US
dc.subjectVoice Onset Timeen_US
dc.subjectVoiceless stop consonantsen_US
dc.titleAutomatic detection of Voice Onset Time in voiceless plosives using gated recurrent unitsen_US
dc.typeArticleen_US


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