Comparison of crisp and fuzzy kNN in phoneme recognition

Despite the advances of information technology tools in the speech recognition task, the challenge to find a rapid and an efficient approach remains a principal research topic. In this paper, we apply the k-nearest neighbors (kNN) algorithm for Timit phoneme recognition with two models: crisp and fuzzy. Essentially, we explore the contribution of the fuzzy aspect for the crisp version of the kNN algorithm. KNN algorithm is characterized by simple implementation, efficiency and speed of execution. The recognition approach consists of extracting a mean reference vector from each phoneme signal in order to assign a crisp or a fuzzy membership degree by measuring the distance to its kNN. The average recognition rate obtained show that kNN algorithm can provide a significant way for the phoneme recognition task particularly using the fuzzy variant.