Accent Detection Task to Classify Accented and Non-Accented Speech
Lorita Damayanti1, Amalia Zahra2

1Lorita Damayanti, Student at Bina Nusantara University (Binus), Jakarta, Indonesia.
2Amalia Zahra, Lecturer at the Master of Information Technology, Binus University, Jakarta, Indonesia.

Manuscript received on 10 August 2019. | Revised Manuscript received on 15 August 2019. | Manuscript published on 30 September 2019. | PP: 8597-8600 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6434098319/2019©BEIESP | DOI: 10.35940/ijrte.C6434.098319

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Abstract: This paper presents a brief survey on accent detection, accent identification, and accent classification. Speech processing has becoming more popular and inspiring expanses lately in signal processing area. It is because speech is one of the most natural form of human communication. However, in processing speech signals intrinsically show many variations even without background noise. Two different person can produce different spectrograms when saying the same sentence. Dialect or Accent is one of the most important factors that can influence the Automatic Speech Recognition or ASR performance besides gender (Unsupervised accent class). Many researches show that dialect or accent in speech can significantly affect the speech system performance. Various methods have been used to increase the accuracy of ASR with accent detection, accent identification, and accent classification. Fused i-vector and Phonotactic are the latest technique that shows a significant degree of accuracy. The purpose of this paper is to briefly survey on accent detection, accent identification, and accent classification and discuss the major improvements made in the past almost 10 years of research.
Keywords: Accent Classification, Accent Detection, Accent Identification, Speech Recognition.

Scope of the Article: Classification