Speaker recognition

About: Speaker recognition is a research topic. Over the lifetime, 14990 publications have been published within this topic receiving 310061 citations.

Emotion recognition from the facial image and speech signal

Abstract: In this paper, we propose an emotion recognition method using the facial images and speech signals Six basic emotions including happiness, sadness, anger, surprise, fear and dislike are investigated Facial expression recognition is performed by using the multi-resolution analysis based on the discrete wavelet Here, we obtain the feature vectors through the LDA (linear discriminant analysis) On the other hand, the emotion recognition from the speech signal method has a structure of performing the recognition algorithm independently for each wavelet subband and the final recognition are obtained from the multi-decision making scheme After merging the facial and speech emotion recognition results, we obtained better performance than previous ones

Voice forgery using ALISP: indexation in a client memory

Abstract: The article deals with a technique of voice forgery using the ALISP (automatic language independent speech processing) approach. Such a technique allows the voice of an arbitrary person (the impostor) to be transformed, forging the identity of another person (the client). Our goal is to demonstrate that an automatic speaker recognition system could be seriously threatened by a transformation of this kind. For this purpose, we use a speaker verification system to calculate the likelihood that the forged voice belongs to the genuine client. Experiments on NIST 2004 evaluation data show that the equal error rate for the verification task is significantly increased by our voice transformation.

Convolutional Neural Network for speaker change detection in telephone speaker diarization system

Abstract: The aim of this paper is to propose a speaker change detection technique based on Convolutional Neural Network (CNN) and evaluate its contribution to the performance of a speaker diarization system for telephone conversations. For the comparison we used an i-vector based speaker diarization system. The baseline speaker change detection uses Generalized Likelihood Ratio (GLR) metric. Experiments were conducted on the English part of the CallHome corpus. Our proposed CNN speaker change detection outperformed the GLR approach, reducing the Equal Error Rate relatively by 46 %. The final results on speaker diarization system indicate that the use of speaker change detection based on CNN is beneficial with relative improvement of diarization error rate by 28 %.