Research Article
Korean Spoken Accent Identification Using T-vector Embeddings
Yong Su Om*,
Hak Sung Kim
Issue:
Volume 13, Issue 2, April 2025
Pages:
13-20
Received:
18 May 2025
Accepted:
12 June 2025
Published:
30 June 2025
DOI:
10.11648/j.sr.20251302.11
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Abstract: In this paper, we introduce a spoken accent identification system for the Korean language, which utilize t-vector embeddings extracted from state-of-the-art TitaNet neural network. To implement the Korean spoken accent identification system, we propose two approaches: First, we introduce a collection method of training data for the Korean spoken accent identification. Korean accents can be broadly classified into four categories: standard accent, southern accent, northwestern accent and northeastern accent. Generally, in Korean language, the speech data for standard accent can be easily obtained via different videos and websites, but the rest of the data except standard accent are very rare and therefore difficult to collect. To mitigate the impact of this data scarcity, we introduce a synthetic audio augmentation using Text-to-Speech (TTS) synthesis techniques. This process is done under the condition that the synthetic audio generated by TTS should be retain accent information of original speaker. Second, we propose an approach to build the deep neural network (DNN) for Korean spoken accent identification in a manner that fine-tune the trainable parameters of a pre-trained TitaNet speaker recognition model by using aforementioned training dataset. Based on the trained TitaNet model, the accent identification is performed using t-vector embedding features extracted from that model, and cosine distance function. The experimental results show that our proposed accent identification system is superior to the systems based on other state-of-the-art DNNs such as the x-vector and ECAPA-TDNN.
Abstract: In this paper, we introduce a spoken accent identification system for the Korean language, which utilize t-vector embeddings extracted from state-of-the-art TitaNet neural network. To implement the Korean spoken accent identification system, we propose two approaches: First, we introduce a collection method of training data for the Korean spoken ac...
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