Optimal trained artificial neural network for Telugu speaker diarization

Abstract

Speaker indexing or diarization is the process of automatically partitioning the conversation involving multiple speakers into homogeneous segments and grouping together all the segments that correspond to the same speaker. So far, certain works have been done under this aspect; still, the need of accurate partitioning process gets lagged under certain criteria. With this in mind, this paper aims to introduce a new speaker indexing or diarization model (Telugu language) that initially involves Mel Frequency Cepstral coefficient based feature extraction. Subsequently, a new Optimized Artificial Neural Network (ANN) is introduced for clustering process. The novelty behind the clustering process is: the training of ANN takes place through optimization logic that updates the weight of ANN by a hybrid concept of Artificial Bee Colony (ABC) and Lion Algorithm (LA). Thereby, the proposed model is named as ANN-ABC-LA model. Finally, the performance of the proposed ANN-ABC-LA model is compared over the state-of-the-art models with respect to different performance measures.