Automatic Speaker Recognition of Identical Twins

<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.

The goal of this paper is to review automatic systems for forensic speaker recognition (FSR) based on scientifically approved methods for calculation and interpretation of biometric evidence. The objective of this paper is not to promote one speaker recognition method against another, but is to make available to the biometric research community data-driven methodology combining automatic speaker recognition techniques and a rigorous forensic experimental background. Forensic speaker recognition is the process of determining if a specific individual (suspected speaker) is the source of a questioned speech recording (trace). This paper aims at reviewing forensic automatic speaker recognition (FASR) methods that provide a coherent way of quantifying and presenting recorded speech as biometric evidence, as well as the assessment of its strength (likelihood ratio) in the Bayesian interpretation framework compatible with interpretations in other forensic disciplines. Forensic speaker recognition has proven an effective tool in the fight against crime, yet there is a constant need for more research due to the difficulties involved because of the within-speaker (within-source) variability, between-speakers (between-sources) variability, and differences in recording sessions conditions.KeywordsLikelihood RatioGaussian Mixture ModelVector QuantizationForensic ScienceSpeaker RecognitionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

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.

Speaker recognition is an advanced method to identify a person from the biometric characteristics of speaking voice samples. Speaker recognition has become a vastly popular and useful research subject with countless essential applications in security, assistance, replication, authentication, automation, and verification. Many techniques are implemented using deep learning and neural network concepts and various datasets for speaker verification and identification. The primary goal of this work is to create improved robust techniques of speaker recognition to identify audio and enhance accuracy to human levels of comprehension. TIMIT and LibriSpeech datasets are used in this paper to develop an efficient automatic speaker recognition system. This work focuses on using MFCC to transform audio to spectrograms without losing the essential features of the audio file in question. We have used a closed set and an open set implementation procedure on these datasets. The closed set implementation uses a standard machine learning convention of utilizing the same datasets for training and testing, leading to higher accuracy. On the other hand, the open set implementation uses one dataset to train and another to test on each occasion. The accuracy, in this case, turned out to be relatively lower. On each dataset, CNN and LSTM deep learning techniques have been used to identify the sound, leading to the observation that implementing CNN resulted in a more significant accuracy.

Acoustical communication is one of the fundamental prerequisites for the existence of human society. Textual language has become extremely important in modern life, but speech has dimensions of richness that text cannot approximate. From speech alone, fairly accurate guesses can be made as to whether the speaker is male or female, adult or child. In addition, experts can extract from speech information regarding e.g. the speaker’s state of mind. As computer power increased and knowledge about speech signals improved, research of speech processing became aimed at automated systems for many purposes. Speaker recognition is the complement of speech recognition. Both techniques use similar methods of speech signal processing. In automatic speech recognition, the speech processing approach tries to extract linguistic information from the speech signal to the exclusion of personal information. Conversely, speaker recognition is focused on the characteristics unique to the individual, disregarding the current word spoken. The uniqueness of an individual’s voice is a consequence of both the physical features of the person vocal tract and the person mental ability to control the muscles in the vocal tract. An ideal speaker recognition system would use only physical features to characterize speakers, since these features cannot be easily changed. However, it is obvious that the physical features as vocal tract dimensions of an unknown speaker cannot be simply measured. Thus, numerical values for physical features or parameters would have to be derived from digital signal processing parameters extracted from the speech signal. Suppose that vocal tracts could be effectively represented by 10 independent physical features, with each feature taking on one of 10 discrete values. In this case, 1010 individuals in the population (i.e., 10 billion) could be distinguished whereas today’s world population amounts to approximately 7 billion individuals. People can reliably identify familiar voices. About 2-3 seconds of speech is sufficient to identify a voice, although performance decreases for unfamiliar voices. One review of human speaker recognition (Lancker et al., 1985) notes that many studies of 8-10 speakers (work colleagues) yield in excess of 97% accuracy if a sentence or more of the test speech is heard. Performance falls to about 54% when duration is shorter than 1 second and/or distorted e.g., severely highpass or lowpass filtered. Performance also falls significantly if training and test utterances are processed through different transmission systems. A study

Automatic systems for forensic speaker recognition (FASR) claim to be largely independent of language based on the fact that feature vectors are composed of acoustic parameters that are derived from the resonance characteristics of vocal tract cavities. Yet a certain ‘language gap’ may remain which may deteriorate the performance of a system unless properly compensated. This forensic aspect of what may be called cross-language speaker recognition has not yet received due attention. Based on the most common forensic cross language setting, the aim of this study was to assess the effect of language mismatch on the performance of a standard FASR system and compare its magnitude with the effect of other sources of mismatch on the same voice data. Using the automatic system Batvox 3 in an experiment with 75 bilingual speakers of seven languages and four kinds of transmission channels, it can be shown that, if speaker model and reference population are matched in terms of language, the remaining mismatch between speaker model and test sample can be neglected, since equal error rates (EERs) for same-language or cross-language comparisons are approximately the same, ranging from zero to 5.6%. Transmission of the speech data via landline telephone, GSM and, for part of the corpus, VoIP (using Skype) caused EERs to rise by less than 1% on average.

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.

Context. The questions of adapting the convolution neural network classifier use in automatic speaker recognition system of critical use(ASRSCU) are considered. The research object is the individual features of the human speech process.Objective. Development of means for separating individual features from the speaker’s speech signal, increasing their informativeness asa result of the factor analysis, their visual representation for the use of the convolution neural network classifier, and optimizing itsarchitecture for the needs of ASRSCU.Method. Measures are proposed to optimize the speaker recognition procedure of the ASRSCU, for which the optimal way of informativefeatures representation and the method of increasing their informativeness are theoretically justified, the topology and measures for increasingof the speaker recognition process efficiency are justified. In particular, it is justified the use of power normalized cepstral coefficients (PNCC)for the description of phonograms recorded in noisy environment conditions. We propose to use Gabor filters to represent information thatwill be analyzed by a convolution neural network, an optimal method of factor analysis (a sparse main components analyzing method) toreduce of the features vector length while preserving its informativeness, an improved topology of the convolution neural network in whichthe Gabor filters are integrated in to the convolution layer, which allows them to optimize their parameters during the neural network trainingprocess, and in a fully connected layer a deep neural network with a bottleneck layer is used, whose weights after training are uses as inputs forthe GMM/HMM control classifier.Results. Methods of representation and optimization of the speaker’s individual features, methods for their visual presentation andimprovement of the topology of a convolution neural network for making speaker recognition on their basis.Conclusions. The obtained theoretical results have found empirical confirmation. In particular, the stability of an improved convolutionneural network to the noisy input phonograms proved to be higher than the results of an ordinary convolution neural network and a deep neuralnetwork. With an SNR increase up to 10 dB, the GMM/HMM classifier is more efficient than the neural network, which can be explained by the efficiency of the used UBM models, but it is much more resource-intensive. Also, the parameters of the Gabor filter bank frames thatprovide the most variable individual features from the speech signal for speaker recognition are determined empirically.

Human perception of sign language can serve as inspiration for the improvement of automatic recognition systems. Experiments with human signers show that sign language signs contain redundancy over time. In this article, experiments are conducted to investigate whether comparable redundancies also exist for an automatic sign language recognition system. Such redundancies could be exploited, for example, by reserving more processing resources for the more informative phases of a sign, or by discarding uninformative phases. In the experiments, an automatic system is trained and tested on isolated fragments of sign language signs. The stimuli used were similar to those of the human signer experiments, allowing us to compare the results. The experiments show that redundancy over time exists for the automatic recognizer. The central phase of a sign is the most informative phase, and the first half of a sign is sufficient to achieve a recognition performance similar to that of the entire sign. These findings concur with the results of the human signer studies. However, there are differences as well, most notably the fact that human signers score better on the early phases of a sign than the automatic system. The results can be used to improve the automatic recognizer, by using only the most informative phases of a sign as input.

Speaker recognition is a technique that automatically identifies a speaker from a recording of their speech utterance. Speaker recognition technologies are taking a new direction due to rapid progress in artificial intelligence. Research in the field of speaker recognition has shown fruitful results. There is, however, not much work done for African indigenous languages that have limited speech data resources. This paper presents how data size impacts the accuracy of an automatic speaker recognition system models, focusing on the Sepedi language as it is one of the South African under-resourced language. The speech data used is acquired from the South African Centre for Digital Language Resources. Four machine learning models, namely, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Multilayer Perceptrons (MLP) and Logistic Regression (LR) are trained under four data setting environment. LR performed better than other models with the highest accuracy of 91% while SVM obtained the highest increase of 4% in accuracy as data size increases.

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.

In recent years, with the popularity of the Internet, digital media has become an indispensable part of people's life. Image acquisition equipment is widely used in daily life and work. However, due to technical problems, it can not meet the performance requirements of users. At the same time, a large number of data and information are difficult to be effectively processed, which also reduces the image quality, reduces the storage space and increases the image distortion. Therefore, it is necessary to develop new algorithms to improve these defects. Therefore, this paper designs and develops an automatic digital media image recognition system based on mean clustering algorithm. Firstly, this paper describes the concept and characteristics of digital media. Then, this paper also studies the application of mean clustering algorithm. Based on this, an automatic digital media image recognition system is designed, and the performance of the system is tested by simulation. Finally, the test results show that the algorithm can accurately identify media images. In general, the performance of the digital media image automatic recognition system based on mean clustering algorithm can meet the needs of target users.

With the increasing improvement of living standards, healthy long-distance running has become one of the current ways of aerobic physical exercise. Long-distance running is beneficial to many bodily functions such as neck, spine, and heart, but inappropriate long-distance running can easily damage the joints of the human body. In order to make more fitness athletes run with a healthy and reasonable posture, this paper studies the automatic recognition and correction system of running movements based on computer vision technology. Based on the analysis of human motion detection methods and human motion recognition methods, an automatic recognition and correction system for running actions is designed. In order to understand the accuracy of the system in the recognition of wrong actions, this paper compares the designed system with the traditional system. The results show that the system designed in this paper has good accuracy and can control the deviation within a reasonable range.