Print Email Facebook Twitter Evaluating the Use of Frequency Masking on a Hybrid Automatic Speech Recognizer for Transitional Dutch Accent of JASMIN-CGN Corpus Title Evaluating the Use of Frequency Masking on a Hybrid Automatic Speech Recognizer for Transitional Dutch Accent of JASMIN-CGN Corpus Author Bălan, Dragos (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Patel, T.B. (mentor) Scharenborg, O.E. (mentor) P. Gonçalves, Joana (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2022-06-22 Abstract There are many experiments conducted with Automatic Speech Recognition (ASR) systems, but many either focus on specific speaker categories or on a language in general. Therefore, bias could occur in such ASR systems towards different genders, age groups, or dialects. But, to analyze and reduce bias, the models require significant amounts of data to be trained on, and some corpora lack that. This is where augmentation techniques can be used to generate more unique data without any further collection of it. This paper explores the use of SpecAugment's frequency masking on such a corpus, JASMIN-CGN, for the Transitional regional accent of Dutch, with a hybrid GMM-HMM architecture, in order to reduce the bias for gender or age, for this specific dialect. The experiments show that SpecAugment does not manage to lower the WER (20.8% overall compared to the baseline model, which achieves 19.5% performance), on the contrary, it even increases the bias for age. The results are mainly attributed to the combination of low amounts of data + the hybrid architecture used, which proves SpecAugment to be a useful augmentation policy only for end-to-end models. Subject ASRhybrid ASRSpeech recognitionBiasDutchJASMIN-CGNaudio augmentationspeech augmentationSpecAugment To reference this document use: http://resolver.tudelft.nl/uuid:a410e9f6-12ac-41be-b415-367e2f7243a3 Part of collection Student theses Document type bachelor thesis Rights © 2022 Dragos Bălan Files PDF Research_paper_Dragos_Final.pdf 1.71 MB Close viewer /islandora/object/uuid:a410e9f6-12ac-41be-b415-367e2f7243a3/datastream/OBJ/view