Automatic speaker recognition system using the discrete Hartley transform and an artificial neural network

<span lang="EN-US">Current Automatic Speaker Recognition (ASR) System has emerged as an important medium of confirmation of identity in many businesses, ecommerce applications, forensics and law enforcement as well. Specialists trained in criminological recognition can play out this undertaking far superior by looking at an arrangement of acoustic, prosodic, and semantic attributes which has been referred to as structured listening. An algorithmbased system has been developed in the recognition of forensic speakers by physics scientists and forensic linguists to reduce the probability of a contextual bias or pre-centric understanding of a reference model with the validity of an unknown audio sample and any suspicious individual. Many researchers are continuing to develop automatic algorithms in signal processing and machine learning so that improving performance can effectively introduce the speaker’s identity, where the automatic system performs equally with the human audience. In this paper, I examine the literature about the identification of speakers by machines and humans, emphasizing the key technical speaker pattern emerging for the automatic technology in the last decade. I focus on many aspects of automatic speaker recognition (ASR) systems, including speaker-specific features, speaker models, standard assessment data sets, and performance metrics</span>

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.

Likelihood ratio calibration is a very important step when applying an automatic speaker recognition system to forensic applications. This paper presents the process and evaluation of a speaker recognition system developed with spoken Indonesian database. The system is developed using MFCC feature, GMM-UBM modeling, and Z normalization. System performances were evaluated based on male and female genders, and two scenarios i.e., natural conversation and interview. System evaluations were done using performance measures for both discrimination and calibration abilities. Results show that based on various indicators, the system behaves very well. This is shown by the best achievable EER values of 4.66%, and Cmc values of 0.04. Therefore, the developed automatic speaker recognition system is now ready to be used for forensic applications in Indonesia.

Real world conditions differ from ideal or laboratory conditions, causing mismatch between training and testing phases, and consequently, inducing performance degradation in automatic speaker recognition systems. Many strategies have been adopted to cope with acoustical degradation; in some applications of speaker identification systems a clean sample of speech, prior to the recognition stage, is needed. This has justified the use of procedures that may reduce the impact of acoustical noise on the desired signal, giving rise to techniques involved in the enhancement of noisy speech. A comparative performance analysis of single-channel (based in classical spectral subtraction and some derived alternatives), dual-channel (based in adaptive noise cancelling) and multi-channel (using microphone arrays) speech enhancement techniques, with different types of noise at different SNRs, as a pre-processing stage to an ergodic HMM-based speaker recognizer, is presented.

Parameters of speech produced during winter weather are analyzed to determine the potential influence of distorted voices due to common cold on automatic speaker recognition systems. This research examines speech recorded before, during, and after bouts with the common cold. ‘‘Cold-affected’’ speech shows noticeable acoustic and phonetic differences between test sessions. Examining the different states of one male speaker’s voice allows an opportunity to address the speaker intersession problem that exists in speaker recognition technology. Specific emphasis is placed on the contrasts in various sessions of TIMIT inspired sentence ‘‘She had your dark suit in greasy wash water all year.’’ It also analyzes phonetic contrasts and looks at differences in formant patterns. Phonetic transcriptions of ‘‘cold’’ and ‘‘normal’’ sessions reflect changes in place of articulation. Perceptual and acoustic analyses reveal pauses and epenthetic syllables that are not constant throughout all sessions. ‘‘Cold-speech’’ also produces noise caused by hoarseness and coughing. Research presented here shows evidence of variability beyond ‘‘normal’’ speech patterns. Contributing to the information on intraspeaker variability, this work provides a preliminary analysis of ‘‘cold-affected’’ speech in an effort to include ‘‘abnormal’’ speech and speech affected by communications disorders into the framework of automatic speaker recognition.

Recognition of a particular speaker amongst a crowded group of people is one of the current areas of interest of researchers. This paper presents a Text Dependent Automatic Speaker recognition system developed and simulated using Matlab. For a better computational efficiency, the system is trained to store voice of the same person under various physiological conditions such as coughing, shouting, during chewing, mouth covered etc. A dictionary is created to store the signature features of each user's voice. A neural networks is then trained using back propagation and accordingly weights are obtained to recognize voice in the testing phase. The efficiency of the proposed system is then compared to the system implemented using vector quantization.

This paper presents an experimental evaluation of the combined temporal and spectral processing methods for speaker recognition task under noise, reverberation or multi-speaker environments. Automatic speaker recognition system gives good performance in controlled environments. Speech recorded in real environments by distant microphones is degraded by factors like background noise, reverberation and interfering speakers. This degradation strongly affects the performance of the speaker recognition system. Combined temporal and spectral processing (TSP) methods proposed in our earlier study are used for pre-processing to improve the speaker-specific features and hence the speaker recognition performance. Different types of degradation like background noise, reverberation and interfering speaker are considered for evaluation. The evaluation is carried out for the individual temporal processing, spectral processing and the combined TSP method. The experimental results show that the combined TSP methods give relatively higher recognition performance compared to either temporal or spectral processing alone.

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.

Acoustical mismatch between training and testing phases induces degradation of performance in automatic speaker recognition systems. Providing robustness to speaker recognizers has to be, therefore, a priority matter. Robustness in the acoustical stage can be accomplished through speech enhancement techniques as a prior stage to the recognizer. These techniques are oriented to the reduction of the impact that acoustical noise produces on the input signal. In this paper, several spectral subtraction-derived techniques are used to enhance single-channel noisy speech. Other perspectives, based in dual-channel (adaptive filtering) and multi-channel (microphone arrays) processing are also presented as optimal solutions to speech enhancement needs. A comparative analysis of the proposed techniques, with different types of noise at different SNRs, as a pre-processing stage to an ergodic HMM-based speaker recognizer, is presented.

This paper analyzes the effects popular audio compression algorithms have on the performance of a speaker recognition system. Popular audio compression algorithms were used to compress both clean and noisy speech before being passed to a speaker recognition system. The features extracted from each speaker were 19-dimensional Mel-Frequency Cepstrum Coefficients (MFCC) and the corresponding features were modeled using a 16 mixture Gaussian Mixture Model (GMM). Our experiments show that compression will have a negative effect on recognition rates if the compressed speech is clean. However, if small amounts of white Gaussian noise are added before the speech is compressed, recognition rates can be increased by as much as 7% with certain compression algorithms.

This chapter describes a method for enhancing the differences between speaker classes at the feature level (feature enhancement) in an automatic speaker recognition system. The original Mel-frequency cepstral coefficient (MFCC) space is projected onto a new feature space by a neural network trained on a subset of speakers which is representative for the whole target population. The new feature space better discriminates between the target classes (speakers) than the original feature space. The chapter focuses on the method for selecting a representative subset of speakers, comparing several approaches to speaker selection. The effect of feature enhancement is tested both for clean and various noisy speech types to evaluate its applicability under practical conditions. It is shown that the proposed method leads to a substantial improvement in speaker recognition performance. The method can also be applied to other automatic speaker classification tasks.

The paper presents the architecture and the results of optimization of selected elements of the Automatic Speaker Recognition (ASR) system that uses Gaussian Mixture Models (GMM) in the classification process. Optimization was performed on the process of selection of individual characteristics using the genetic algorithm and the parameters of Gaussian distributions used to describe individual voices. The system that was developed was tested in order to evaluate the impact of different compression methods used, among others, in landline, mobile, and VoIP telephony systems, on effectiveness of the speaker identification. Also, the results were presented of effectiveness of speaker identification at specific levels of noise with the speech signal and occurrence of other disturbances that could appear during phone calls, which made it possible to specify the spectrum of applications of the presented ASR system.

A Survey of Automatic Speaker Recognition System Using Artificial Neural Networks

We developed a CNC machine’s condition monitoring system based on Artificial Neural Network (ANN) and correlate the same with the real-time CNC machine data. The classification of the condition of a CNC machine was done by deciding whether it is a fresh machine or worn machine using machine learning techniques. In consideration of real time data loaded from a CNC machine we built a database and then modelled an ANN. Based on this approach of machine learning which implements pattern recognition and probabilistic modelling of the CNC machine data, classification of the condition of a CNC machine was done successfully. Finally, performance analysis of this machine model prevail in terms of Machine Error Rate (MER) upholds the impressive fact that modeling using the ANN yields better results over another alternative modeling techniques and can be used for developing Automatic CNC machine condition monitoring and recognition system.

Automatic speaker recognition systems typically rely on parameters derived from resonance features of the vocal tract. Th is implies that the more similar the geometry of two vocal tracts is, the more similar will be the respective similarity coeffi cients, or likelihood ratios (LRs). Quite obviously this problem is particularly relevant to related speakers, most extremely for identical (monozygotic) twins. Th is paper is about an experiment with 9 male and 26 female pairs of identical twins who produced one read and one spontaneous speech sample. An automatic system for forensic speaker recognition (Batvox 3.1) was used to calculateinter-speaker (non-target), (2) intratwin pair, and (3) intra-speaker (target) LR distributions. Results show that in certain conditions an automatic Bayesian-based system is capable of distinguishing even the vast majority of very similar sounding voices such as those of identical twins. However, the performance of the system used here was superior for male as compared to female voices. Quite obviously the sex-related diff erence was enhanced by the genetic similarity factor.