Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/20.500.12421/4532
Title: Automatic detection of Voice Onset Time in voiceless plosives using gated recurrent units
Authors: Arias Vergara, Tomás
Argüello Vélez, Patricia
Vásquez Correa, Juan Camilo
Nöth, Elmar
Schuster, Maria Elke
González Rátiva, Marí­a Claudia
Orozco Arroyave, Juan Rafael
Keywords: Diadochokinesis
Recurrent neural network
Voice Onset Time
Voiceless stop consonants
Issue Date: 27-May-2020
Publisher: Elsevier Inc.
Abstract: Voice 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.
URI: http://repository.usc.edu.co/handle/20.500.12421/4532
ISSN: 1051-2004
Appears in Collections:Artículos Científicos

Files in This Item:
File Description SizeFormat 
Automatic detection of Voice Onset Time in voiceless.png85.11 kBimage/pngView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.