Dialect classification based on the speed and the pause of speech utterances*

Abstract

In this paper, we propose an approach for dialect classification based on the speed and pause of speech utterances as well as the age and gender of the speakers. Dialect classification is one of the important techniques for speech analysis. For example, an accurate dialect classification model can potentially improve the performance of speaker or speech recognition. According to previous studies, research based on deep learning using Mel-Frequency Cepstral Coefficients (MFCC) features has been the dominant approach. We focus on the acoustic differences between regions and conduct dialect classification based on the extracted features derived from the differences. In this paper, we propose an approach of extracting underexplored additional features, namely the speed and the pauses of speech utterances along with the metadata including the age and the gender of the speakers. Experimental results show that our proposed approach results in higher accuracy, especially with the speech rate feature, compared to the method only using the MFCC features. The accuracy improved from 91.02% to 97.02% compared to the previous method that only used MFCC features, by incorporating all the proposed features in this paper.

Improved Gender Detection and Age Estimation Using Multimodal Speech Datasets for speech Age Classification

“…Backend algorithms show better performance when training and test utterance lengths are equal for longer speech segments, and using medium-length utterances for training and longer ones for testing works well for female datasets. A study was conducted by Jonghwan Na․ and Bowon Lee (Na & Lee, 2023) introduced a novel approach for dialect classification in speech analysis. The method incorporates acoustic differences between regions, extracting additional features like speech speed, pauses, age, and gender of speakers.…”