Overview of speech enhancement techniques for automatic speaker recognition

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.

<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.

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.

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.

We investigate the use of Fourier plane nonlinear filtering for phase-encoded images. We investigate the performance of the nonlinear joint transform correlator and the nonlinearly transformed matched filter for phase-encoded images with different types of input noise. We use the peak-to-output-energy ratio, peak-to-sidelobe ratio, and discrimination ratio as the metrics for measuring the performances. We mathematically analyze the peak-to-output-energy ratio of the nonlinearly transformed matched filter for phase-encoded images with spatially nonoverlapping white noise. Computer simulations are provided to show the performance improvements of the nonlinear filtering techniques for the phase-encoded images. In comparison with linear filtering techniques, we find that the nonlinear filtering techniques substantially improve the performance metrics. From the computer-simulation results it can be seen that the nonlinear joint transform correlator performs better than the nonlinearly transformed matched filter in detecting phase-encoded targets in the presence of different types of noise, such as additive overlapping white noise, spatially nonoverlapping white background noise, spatially nonoverlapping colored background noise, and nontarget objects.

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.

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.

Recent advancements in speaker verification techniques show promise, but their performance often deteriorates significantly in challenging acoustic environments. Although speech enhancement methods can improve perceived audio quality, they may unintentionally distort speaker-specific information, which can affect verification accuracy. This problem has become more noticeable with the increasing use of generative deep neural networks (DNNs) for speech enhancement. While these networks can produce intelligible speech even in conditions of very low signal-to-noise ratio (SNR), they may also severely alter distinctive speaker characteristics. To tackle this issue, we propose a novel neural network framework that effectively combines speaker embeddings extracted from both noisy and enhanced speech using a Siamese architecture. This architecture allows us to leverage complementary information from both sources, enhancing the robustness of speaker verification under severe noise conditions. Our framework is lightweight and agnostic to specific speaker verification and speech enhancement techniques, enabling the use of a wide range of state-of-the-art solutions without modification. Experimental Results demonstrate the superior performance of our proposed framework.

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.

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.

Oscillator noise is generally modeled by a power law spectral density. Thus it is possible to characterize different noise sources, each of them corresponding to a particular power law. The measurement of the contribution of these sources is necessary to know their origin and to remedy these causes in order to improve oscillator performance. Usually, an estimation of the different types of noise present in a signal is obtained by using a variance. However, the sensitivity of these variances differs for each type of noise and then limits this method. On the other hand, the use of several variances, each of them more sensitive to one type of noise, permits one to notably improve the measurement accuracy. The method suggested here uses as many different variances as there are types of noise to measure. The improvement of measurement accuracy of the noise coefficient is discussed in this paper.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this work, the effects of background noises on nonlinear dynamics of a modelled standing-wave thermoacoustic system with subcritical Hopf bifurcation behaviors are studied. These noises include (1) pressure-coupled (acoustic), (2) velocity-coupled (flow), and (3) external combustion noise. It is found that these three types of background noises play important, but different roles in changing the hysteresis width and stability boundary. In addition, the stochastic transition from stability to instability is investigated, as the noise intensity is varied. Two different stochastic P-bifurcations are identified. One is related to a craterlike probability density distribution. The other is associated with a probability density distribution characterized with two peaks and one trough. With each type of noise affecting the system's stochastic behaviors being evaluated, the effect of two different types of noises is then studied. It is shown that the combined noises (types 1 and 2) cannot only destabilize global stable thermoacoustic system, but also stabilize linearly unstable system. This depends strongly on the superimposition form of the two types of noises. In addition, when the thermoacoustic system is disturbed by the combined noise (types 3 and 1 or types 3 and 2), the transition process is dominated by the combustion noise.

Objective: to analyze speech perception in normally hearing adults when listening in silence and with different types of noise. Methods: 40 individuals of both sexes, aged 18 to 45 years, participated in the study. Speech perception was assessed with the Lists of Sentences in Portuguese test, without a competing noise and with speech-spectrum, babble, and cocktail party noise. A mixed-effects linear regression model and the 95% confidence interval were used. Results: the subjects’ performance was worse in the three types of noise than in silence. When comparing the types of noise, differences were found in all combinations (speech-spectrum X babble, speech-spectrum X cocktail party, and babble X cocktail party), with a worse performance in babble, noise, followed by cocktail party. Conclusion: all noises negatively influenced speech perception, with a worse performance in babble, followed by cocktail party and speech-spectrum.