DEVELOPMENT OF VOICE ACTIVATED SPEAKER RECOGNITION SYSTEM.

Information Technology And Control, 2020

One extension of feature vector for automatic speaker recognition is considered in this paper. The starting feature vector consisted of 18 mel-frequency cepstral coefficients (MFCCs). Extension was done with two additional features derived from the spectrum of the speech signal. The main idea that generated this research is that it is possible to increase the efficiency of automatic speaker recognition by constructing a feature vector which tracks a real perceived spectrum in the observed speech. Additional features are based on the energy maximums in the appropriate frequency ranges of observed speech frames. In experiments, accuracy and equal error rate (EER) are compared in the case when feature vectors contain only 18 MFCCs and in cases when additional features are used. Recognition accuracy increased by around 3%. Values of EER show smaller differentiation but the results show that adding proposed additional features produced a lower decision threshold. These results indicate t...

International Journal of Electrical and Computer Engineering (IJECE), 2022

In this research, we present an automatic speaker recognition system based on adaptive orthogonal transformations. To obtain the informative features with a minimum dimension from the input signals, we created an adaptive operator, which helped to identify the speaker's voice in a fast and efficient manner. We test the efficiency and the performance of our method by comparing it with another approach, mel-frequency cepstral coefficients (MFCCs), which is widely used by researchers as their feature extraction method. The experimental results show the importance of creating the adaptive operator, which gives added value to the proposed approach. The performance of the system achieved 96.8% accuracy using Fourier transform as a compression method and 98.1% using Correlation as a compression method.

Automatic Speaker Identification technology has recently been implemented in several of commercial areas successfully. Speaker identification comes under Speaker recognition and is gaining significance for voice based biometrics. It is used in appliances that understand voice commands, provides security to confidential information,etc. In this paper we have built reference model for each speaker using the acoustic features .Testing has been done by comparing the features of the test sample with the reference model.We have used MFCC technique for feature extraction .For creating the reference model Vector Quantization( VQ) with LGB (Linde, Buzo, and Gray)was used. For testing VQ distortion was used.The system achieved 85% accuracy.

Speaker recognition (SR) is a dynamic biometric task. SR is a multidisplinary problem that encompasses many aspects of human speech, including speech recognition, language recognition, and speech accents. This technique makes it possible to use the speaker’s voice to verify his/her identity and provide controlled access to services. The Mel-frequency extraction method is leading approach for speech feature extraction. In this thesis, a new algorithm has been proposed which incorporates FVQ and DCT based MFCC feature extraction method. The proposed system will be improved the performance of SR through MFCC and FVQ methods. The FVQ performance result will be compared with K means quantization in terms of EER

—The performance of any speaker recognition system depends on the duration of the speech samples. The higher the number of feature vectors is, the better is the efficiency. A major contribution of this paper is in enhancing the identification accuracy of the speaker recognition system through minimization of the objective function and associated distortions. With nonlinear mapping, the sectional set fuzzy vector quantization with novel norm is utilized here as usual to form speaker's model in the high-dimensional feature space. However, during feature extraction, the traditional triangular shaped bins have been replaced by Gaussian shaped filter (GF) and Tukey filter (TF) for calculating the mel frequency cepstral coefficients (MFCCs). The paper presents experimental evaluation of three modeling techniques, viz. fuzzy c-means, fuzzy vector quantization (FVQ)2 and novel fuzzy vector quantization (NFVQ). On simulation, the NFVQ shows significant improvement in performance over fuzzy c-means and FVQ2. The experimental evidence demonstrates that for two seconds of training and one second of testing data, the efficiency of the NFVQ, with a minimum objective function of = 0.073 and distortion D = 4.334, for a set of 100 speakers chosen from the Texas Instruments and Massachusetts Institute of Technology (TIMIT) database and self-collected database is 98.8% and 98.1%, respectively. Index Terms—Gaussian filter (GF); mel frequency cepstral coefficients (MFCCs); novel fuzzy vector quantization (NFVQ); triangular filter (TF); Tukey filter.

2012 International Conference on Communication, Information & Computing Technology (ICCICT), 2012

Speaker identification system is one of the applications of biometric using voice signal. In this paper we have implemented a speaker recognition system using a combination of Mel Frequency Capestral Coefficients (MFCC) & Kekre's Median Codebook Generation Algorithm (KMCG). The MFCC algorithm is used for feature extraction while the KMCG algorithm plays important role in code book generation and feature matching. For implementation simplicity the system is built as a text dependent system, i.e. common text used by all users. KMCG algorithm provides implementation simplicity along with high level of accuracy.

Lecture Notes in Computer Science, 2008

Abstract. Speaker Recognition is the process of identifying a speaker by analyzing spectral shape of the voice signal. This is done by extracting & matching the feature of voice signal. Mel-frequency Cepstrum Co-efficient (MFCC) is the feature extraction technique in which we will get ...

Speech processing is emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. Feature extraction is the first step for speaker recognition. Many algorithms are suggested/developed by the researchers for feature extraction. In this work, the Mel Frequency Cepstrum Coefficient (MFCC) feature has been used for designing a text dependent speaker identification system. BPNN is used for identification of speaker after training the feature set from MFCC. Some modifications to the existing technique of MFCC for feature extraction are also suggested to improve the speaker recognition efficiency. Information from speech recognition can be used in various ways in state-of-the-art speaker recognition systems. This includes the obvious use of recognized words to enable the use of text-dependent speaker modeling techniques when the words spoken are not given. Furthermore, it has been shown that the choice of words and phones itself can be a useful indicator of speaker identity. Also, recognizer output enables higher-level features, in particular those related to prosodic properties of speech.

International Journal of Speech Technology, 2012

In this paper, a new and novel Automatic Speaker Recognition (ASR) system is presented. The new ASR system includes novel feature extraction and vector classification steps utilizing distributed Discrete Cosine Transform (DCT-II) based Mel Frequency Cepstral Coefficients (MFCC) and Fuzzy Vector Quantization (FVQ). The ASR algorithm utilizes an approach based on MFCC to identify dynamic features that are used for Speaker Recognition (SR). A series of experiments were performed utilizing three different feature extraction methods: (1) conventional MFCC; (2) Delta-Delta MFCC (DDMFCC); and (3) DCT-II based DDMFCC. The experiments were then expanded to include four classifiers: (1) FVQ; (2) K-means Vector Quantization (VQ); (3) Linde, Buzo and Gray VQ; and (4) Gaussian Mixed Model (GMM). The combination of DCT-II based MFCC, DMFCC and DDMFCC with FVQ was found to have the lowest Equal Error Rate for the VQ based classifiers. The results found were an improvement over previously reported non-GMM methods and approached the results achieved for the computationally expensive GMM based method. Speaker verification tests carried out highlighted the overall performance improvement for the new ASR system. The National Institute of Standards and Technology Speaker Recognition Evaluation corpora was used to provide speaker source data for the experiments.

Speech feature extraction is the most significant step in any Automatic speaker recognition system. In the last 60 years a lot of research has gone into parametric representation of these speech features. Several techniques are currently being used for Automatic Speaker Recognition. Yet Automatic Speaker Recognition still remains a confront mainly due to variations in speaker's vocal tract with time and health, varying environmental conditions, disparities in the behavior and quality of speech recorders etc. MFCC is a extensively used technique in Automatic speaker recognition. In this paper the performance of MFCC technique was evaluated in a quiet environment. A speaker database containing 30 male and 30 female speakers was created. Two separate experiments were conducted for the performance evaluation of MFCC technique when applied to K means clustering. In the first case the speech features were directly matched. In the second case a VQ codebook was created by clustering the training features of these 60 speakers. A distortion easure based on the minimum Euclidean distance was used for speaker recognition. The failure rate of speaker recognition in first ase was found to be was found to be 10% while in the second case as found to be 14%. Matlab-7.10.0 was used for this study