Native and non-native class discrimination using speech rhythm- and auditory-based cues. (May 2015)

• Record Type: Journal Article
• Authors: Selouani, S.-A.; Alotaibi, Y.; Cichocki, W.; Gharsellaoui, S.; Kadi, K.
• Is Part Of: Computer speech & language. Volume 31(2015)
• Page Start: 28
• Page End: 48
• Publication Date: 2015-05
• Subjects: Speech rhythm -- Auditory cues -- Duration -- Intensity -- SVMs -- GMMs -- Hybrid GMM/SVM -- Logistic regression; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2014.11.003
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract : Highlights: New models of speech rhythm and auditory knowledge are proposed. Use both duration and intensity metrics to classify native and non-native accents. Perform accent classification by logistic regression (LR) and with baseline systems. LR-based approach provides the best classification of native/non-native Arabic speech. Combination of auditoryc indicative features and rhythm metrics provides the best classification. Abstract: In recent years, the use of rhythm-based features in speech processing systems has received growing interest. This approach uses a wide array of rhythm metrics that have been developed to capture speech timing differences between and within languages. However, the reliability of rhythm metrics is being increasingly called into question. In this paper, we propose two modifications to this approach. First, we describe a model that is based on auditory cues that simulate the external, middle and inner parts of the ear. We evaluate this model by performing experiments to discriminate between native and non-native Arabic speech. Data are from the West Point Arabic Speech Corpus; testing is done on standard classifiers based on Gaussian Mixture Models (GMMs), Support Vector Machines (SVMs) and a hybrid GMM/SVM. Results show that the auditory-based model consistently outperforms a traditional rhythm-metric approach that includes both duration- and intensity-based metrics. Second, we propose a framework that combines the rhythm metrics and the auditory-based cues in the context of a Logistic Regression (LR) method that can optimize feature combination. Further results show that the proposed LR-based method improves performance over the standard classifiers in the discrimination between the native and non-native Arabic speech.