Speaker Recognition in Emotional Environment using Excitation Features

Abstract

Speaker Recognition is known as the task of recognizing the person speaking from his/her speech. Speaker recognition has many applications including transaction authentication, access control, voice dialing, web services, etc. Emotive speaker recognition is important because in real life, human beings extensively express emotions during conversations, and emotions alter the human voice. A text-independent speaker recognition system is proposed in the work. The system designed is for emotional environment. The proposed system in this work is trained using the speech samples recorded in neutral environment and the system evaluation is performed in an emotional environment. Here, excitation source features are used to represent speakerspecific details contained in speech signal. The excitation source signal is obtained after separating the segmental level features from the voice samples. The excitation source signal is almost considered as a noise so identifying a speaker in an emotive environment is a challenging task. Excitation features include Linear Prediction (LP) residual, Glottal Closure Instance (GCI), LP residual phase, residual cepstrum, Residual Mel-Frequency Cepstral Coefficient (R-MFCC), etc. A decrease in performance is observed when the system is trained with neutral speech samples and tested with emotional speech samples. Different emotions considered for emotional speaker identification are happy, sad, anger, fear, neutral, surprise, disgust, and sarcastic For the classification of speakers the algorithms used are Gaussian Mixture Model (GMM), Support Vector Machine (SVM), K-Nearest Neighbor(KNN), Random Forest and Naive Bayes.

Enhancement in Speaker Identification through Feature Fusion using Advanced Dilated Convolution Neural Network

“…Extracting the features of both the excitation and vocal tract system is required. Various approaches are used to extract excitation information [17,18]. The authors of [13,17] elaborated on the three excitation features based on strength, energy and derived frequency around Glottal Closure Instant (GCI) locations.…”

“…The obtained LP residual signal or glottal excitation suppresses vocal tract characteristics. The glottal excitation phase, or the residual phase, is unique for each individual, and thus the feature is helpful in recognition applications [18]. This feature can be extracted by taking the cosine phase of an analytic signal using the Hilbert transform e h (n).…”

Development of a secure multi-factor authentication algorithm for mobile money applications

“…Voice recognition identifies people using their unique voice acoustic features, matched with the copy of the digital template stored in the database (Thomas et al, 2020;Khotimah et al, 2020;Ye & Yang, 2021). Voice recognition determines the person's identity based on the voice, not the content of the speech (Kandhari et al, 2018;Al-Tekreeti & Ibrahim, 2020).…”

“…Voice recognition is applied in various fields, including telebanking, video games, household appliances, surveillance and security systems, mobile application, voice dialling, online services, personal assistant services, and access control (Khotimah et al, 2020;Thomas et al, 2020;Krčadinac et al, 2021;Ye & Yang, 2021). Voice recognition is also applied in mobile money systems.…”

“…Voice recognition offers several benefits, such as the uniqueness of the human voice, security in access control, not requiring new hardware, and good compatibility (Thomas et al, 2020;Khotimah et al, 2020). Nevertheless, voice recognition is sensitive to background noise, harsh environments, playback spoofing, emotional states, voice changes over time, characteristics of the microphones used, accent differences, and quality of the communication channel during enrolment and authentication (Krčadinac et al, 2021;Cayir & Navruz, 2021).…”