Accent detection task to classify accented and non-accented speech

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.