Segmentation Of Speech Signal Research Articles

Singular spectral analysis-based interpolation for missing segments of speech signals extracted from videos captured by dual rolling-shutter cameras

Recent studies have been proposed to extract speech signals from captured videos of objects vibrating by sound waves. Among them, a method for extracting speech signals from videos captured by a rolling-shutter camera, which is widely used, has been attracting attention. A rolling-shutter camera records image data in one row of pixels at a time, thereby capturing the vibration of objects caused by sound waves. However, there are time intervals between frames of the videos, resulting in missing segments in the extracted speech signals. The conventional method uses an autoregressive model to interpolate these missing segments. However, the conventional method ignores the noise in the extracted speech signals, and therefore the noise remains. In this paper, we propose a method to interpolate missing segments based on singular spectrum analysis, which considers the noise to further improve speech quality, by dual rolling-shutter cameras. By using the singular spectral analysis, the missing segments can be determined using only the speech components in the signals, which are related to large singular value, thereby reducing the noise. Experimental results show that the proposed method outperforms the conventional methods in terms of quality and intelligibility of the extracted speech signals.

Rule Based Speech Signal Segmentation

This paper presents the automated speech signal segmentation problem. Segmentation algorithms based on energetic threshold showed good results only in noise-free environments. With higher noise level automatic threshold calculation becomes complicated task. Rule based postprocessing of segments can give more stable results. Off-line, on-line and extrema types of rules are reviewed. An extrema-type segmentation algorithm is proposed. This algorithm is enhanced bya rule base to extract higher energy level segments from noise. This algorithm can work well with energy like features. The experiments were made to compare threshold and rule-based segmentation in different noise types. Also was tested if multifeature segmentation can improve segmentation results. The extrema rule-based segmentation showed smaller error ratio in different noise types and levels. Proposed algorithm does not require high calculation resources. Such algorithm can be processed by devices with limited computing power.

Noise-resistant algorithm of speech signal segmentation in subscriber identification systems

Noise-resistant algorithm of speech signal segmentation in subscriber identification systems

Analysis of Speech Cryptography Based on Segmentation and Chaotic Logistic Map Keys

Enhancing speech cryptography performance by increasing the cryptography process speed (throughput) is a vital study. In this research paper a detailed study and analysis will be performed to find a compromised solution of dividing the speech signal into segments with various lengths. For each segment a CLK will be generated to apply segment encryption-decryption, the encrypted-decrypted segments will be combined to form the encrypted-decrypted speech signal. The proposed method will use a complicated PK which contains information about the segments and the chaotic parameters, this information will be used for speech segmentation and segments CLKs to be generated. A simple and easy to implement method of speech cryptography will be introduced; this method will be based on speech signal segmentation. The proposed method will be implemented and the obtained results will be used to perform various data analysis method to prove the quality, security and efficiency provided by the proposed method.

A Wavelet-Based Approach to Rapidly Identify Drug-Addicted Individuals Using Voice Signal Analysis

Recognizing and classifying signals plays a pivotal role across diverse disciplines such as classification, pattern recognition, data preprocessing, and predictive science. This study specifically investigates the use of Haar and Symlet (Sym2) wavelet transforms in analyzing short voice signals to distinguish between drug-addicted and non-addicted individuals. The primary aim is to uncover distinctive features in voice signals that correlate with addiction. Haar and Symlet wavelet transforms are employed to process substantial segments of speech signals, extracting meaningful insights that can aid law enforcement agencies and addiction researchers. Each signal undergoes visualization and analysis to unveil patterns and discrepancies brought to light by different wavelet transformations. Following the wavelet transformation process, the study evaluates the Peak Signal-to-Noise Ratio (PSNR) and Signal-to-Noise Ratio (SNR) using MATLAB's wavelet toolbox. These metrics serve as crucial indicators for decision-making processes aimed at identifying drug-addicted individuals based on unique voice signal characteristics. Beyond advancing signal processing techniques, this research aims to have practical applications in recognizing behavioral patterns associated with addiction. Ultimately, the goal is to leverage these insights to develop more effective intervention and support strategies within relevant communities and healthcare settings. By integrating wavelet analysis with voice signal processing, this study not only enhances our understanding of addiction-related behaviors but also contributes valuable tools for real-world applications in law enforcement and healthcare sectors. These efforts are geared towards fostering more targeted and efficient responses to addiction issues in society.

A Method of Increasing the Accuracy of Segmentation of a Speech Signal Based on Its Fractal Characteristics

The paper considers the analysis of approaches to the segmentation of the speech signal into vocalized and not vocalized fragments. The necessity of improving the accuracy of segmentation due to a more accurate description of the process of speech signal representation is shown. A rational method for calculating the fractal dimension to increase the accuracy of the speech signal segmentation process has been determined.

Automatic Database Segmentation using Hybrid Spectrum -Visual Approach

Nowadays automated segmentation of speech signals has been attracted many of researchers all-over the world, Many speech processing systems require segmentation of speech waveform into principal acoustic units. In this research, TIMIT DataBase (DB) is utilized to carry on this process and justify its operation or results. Thus, this paper presents a novel method of segmentation of speech phonemes, where the proposed strategy helps in the selection of appropriate feature extraction technique for speech segmentation. There are three main techniques of feature extraction used in our research; the first technique is the Mel Frequency Cepstral Coefficient (MFCC), the second technique is known by Best Tree Encoding (BTE), while the third is Image Normalized Encoder (INE), which is a hybrid technique between the Best Tree Image (BTI), and the Convolution Neural Network (CNN) ResNet-50. Then, data are trained using a hybrid model that consists of Hidden Markov Model (HMM), and Gaussian Mixture Model (GMM) to improve the performance of automatic speech recognition. The proposed model is tested and verified against the most widely used feature Mel Frequency Cepstral Coefficient (MFCC) plus delta and delta-delta coefficients (39 parameters) to evaluate its performance. This approach has the potential to be used in applications such as automatic speech recognition and automatic language identification. The experimental results show that BTE technique achieved the highest success rate (𝜂) (92.64%) than using the (INE) technique. However, the INE technique gives confusion success rate for Tr and NTr of values 97.1% and 99.1%, respectively.

Speech to multi-language text conversion

Right from the beginning of previous century, researchers have shown interest in areas like Automatic Speech Recognition, Image Processing and Natural Language Processing. The area of Automatic Speech Recognition (ASR) has received attention over the past five decades due to its application in both commercial and military. In the recent times this can be attributed to the advancements in Artificial Intelligence and Advanced Algorithms. ASR takes speech as input and converts it in to text. ASR is employed in electronic dictionaries, Customer Call Centers, Voice Dictation and Query based Information Systems, Speech Transcription, Avionics, Smart Houses and Access Systems and many more areas. ASR can also be used to interact with handicapped people. ASR enables human beings interact with computers using speech rather than using keyboards & mouse (Vimalaand Radha V., 2012). ASR aims to provide natural machine interface where in speech acts input to the machine. Generally, ASR is based on two tasks viz. Identification of Phoneme and Whole-Word Decoding. A relationship between speech signal and speech segment that has dissimilar physical or perceptual features usually termed as phones is established in two steps. The first step deals with dimensionality reduction and second step deals with the estimation of likelihood of each phoneme. In the dimensionality reduction phase, the volume of the speech signal is decreased by extracting the relevant information using task-specific knowledge. In the next phase, the system recognizes the word sequence using a discriminative program. Traditionally ASR systems preferred the Mel frequency Cepstral coefficients (MFCC) for the first phase and Discriminative techniques for the second phase. Over the years ASR systems have evolved from being an integration of multiple trained components to “end-to-end” Deep neural architectures that link speech to text directly. The proposed work implements an MLP with AdaBoost Classifier. The MLP will be used to extract discriminative features from the speech data. Later AdaBoost classifier will map these features to the relevant set of words.

A NOVEL SPEECH RECOGNITION SYSTEM USING FUZZY NEURAL NETWORK

An important field in digital speech processing is human voice. Speech recognition in humans has long been a hot topic in signal processing and artificial intelligence research. Natural Language Processing (NLP) offers an interdisciplinary subfield called speech recognition that makes it easier for machines to recognize spoken language and transform it into text. To communicate with machines, speech recognition machines has the potential to be helpful. Technology that can have communicates in real time has been made possible. However, there remain additional issues, such as speaker variance due to other factors like age, gender, signal speed, pronunciation variations, noise from the surrounding area etc. Classification of age and gender is important for speech processing. A lot of work has been done to enhance each of these phases in order to get better and more accurate results. The main goal of this analysis is on the integration of machine learning into the speech recognition system. Hence, this analysis presents a novel speech recognition system using Fuzzy neural network. Pre-processing, speech signal segmentation, speech feature extraction, and speaker recognition are the different phases of a speech recognition system. It uses a fuzzy neural network to identify the speaker's age and gender. Accuracy, F1-score, and Precision are used to evaluate the fuzzy model's performance.

Spike Detection Based on the Adaptive Time–Frequency Analysis

This paper presents a novel spike detection algorithm in nonstationary signals using a time–frequency (t–f) approach. The proposed algorithm exploits the direction of signal energy in the t–f domain to detect spikes in the presence of high-frequency nonstationary signals even at low signal-to-noise ratio. The performance of the proposed approach is evaluated using synthetic nonstationary signals, synthesized signals mimicking electroencephalogram (EEG) signals, manually selected segments of speech signals, and manually selected segments of real EEG signals. The statistical measures, such as hit rate and precision, are used to demonstrate that the proposed algorithm performs better than other widely used algorithms, such as the smoothed nonlinear energy detector.

Method for Measuring the Intelligibility of Speech Signals in the Kullback–Leibler Information Metric

We consider the problem of determination of the intelligibility of speech of a speaker according to a finite fragment of the speech signal. It is shown that the main difficulties in the solution of this problem are connected with the necessity of analysis of small samples. To overcome the problem of small samples, we proposed a new high-speed method for measuring the intelligibility of speech signals on the sonic level of its perception. The proposed method is based on the information indicator of speech intelligibility in the Kullback-Leibler metric. We consider an example of practical realization of the new method with the use of a self-regression model of minimum sound units from the speech flow of a speaker. The characteristics of efficiency of the new method are analyzed. It is shown that, under certain conditions, the application of the information indicator enables us to realize the general systems principle of guaranteed result. On the basis of the software developed by the authors, we designed and performed full-scale experiments and established quantitative estimates for the speed of this method. It is shown that, with the help of this method, quite accurate and reliable estimates of the information indicator are obtained for short (2–3 min) segments of speech signals. The accumulated results and the conclusions made on their basis are intended for applications in the development of new systems and improvement of the existing systems of automatic speech processing and recognition intended for the operation in the real-time mode.

On the Minimal Overcompleteness Allowing Universal Sparse Representation

Sparse representation over redundant dictionaries constitute a good model for many classes of signals ( e.g. , patches of natural images, segments of speech signals, and so on). However, despite its popularity, a very little are known about the representation capacity of this model. In this paper, we study how redundant a dictionary must be so as to allow any vector to admit a sparse approximation with a prescribed sparsity and a prescribed level of accuracy. We address this problem, both in a worst-case setting and in an average-case one. For each scenario, we derive lower and upper bounds on the minimal required overcompleteness. Our bounds have simple closed-form expressions that allow to easily deduce the asymptotic behavior in large dimensions. In particular, we find that the required overcompleteness grows exponentially with the sparsity level and polynomially with the allowed representation error. This implies that universal sparse representation is practical only at moderate sparsity levels, but can be achieved with a relatively high accuracy. As a side effect of our analysis, we obtain a tight lower bound on the regularized incomplete beta function, which may be interesting in its own right. We illustrate the validity of our results through numerical simulations, which support our findings.

A Method for Measuring the Pitch Frequency of Speech Signals for the Systems of Acoustic Speech Analysis

We developed a new method for measuring the pitch frequency of speech signals with elevated noise immunity. The problem of protection against intense background noise is solved in this method by the frequency selection of vocalized segments of speech signals according to a scheme with comb filter of interperiodic accumulation. The efficiency of the method is analyzed both theoretically and experimentally with the help of a multichannel frequency meter intended for the acoustic speech analysis. It is shown that, for a signal-to-noise ratio of 10 dB and higher, the error of the method does not exceed 2%.

Phoneme boundary detection from speech: A rule based approach

In this paper, a novel approach has been proposed for the automatic segmentation of speech signal into phonemes. In a well spoken word, phonemes can be characterized by the changes observed in speech waveform. To get phoneme boundaries, the signal level properties of speech waveform i.e. changes in the waveform during transformation from one phoneme to the other are explored. The problem of phoneme level segmentation has been addressed in this work from two aspects 1. Segmentation of phonemes between voiced and unvoiced portions and 2. Segmentation of phonemes within voiced and unvoiced regions. Pitch and zero-frequency filter signal are used to get the region of change from voiced to unvoiced and vice versa. The segmentation of phoneme boundaries within voiced and unvoiced regions are approximated using the properties of power spectrum of correlation of adjacent frames of the signal. A finite set of rules is proposed on the variations observed in the power spectrum during phoneme transitions. The segmentation results of both approaches are combined to get the final phoneme boundaries. Three databases namely TIMIT Corpus, IIIT Hyderabad Marathi database & IIIT Hyderabad Hindi database (IIIT-H Indic Speech Databases) are used to test the proposed approach; an accuracy of 95.40%, 96.87% and 96.12% is achieved within the tolerance range of 10 ms respectively. The results of the proposed approach are observed to give precise phoneme boundaries.

Speech Signal Segmentation into Vocalized and Unvocalized Segments on the Basis of Simultaneous Masking

This paper touches upon a model of simultaneous acoustic masking, which detects speech signal components perceived by a human’s auditory system. A simultaneous masking algorithm on the basis of this model is proposed. It is shown that, after simultaneous masking, a signal becomes a binary structure that reflects the harmonic structure of a vocalized sequence. It is experimentally proven that this structure can be used to detect key speech segments (from the standpoint of perception by an auditory system). This structure serves as a basis for an algorithm of high-quality segmentation of a speech signal into vocalized and unvocalized segments, which does not require learning before use. The joint use of the algorithms for simultaneous masking and speech signal segmentation is tested, and their performance is evaluated.

Algorithms of segmentation of speech signal on the correlated noise background

Algorithms of segmentation based on formant and antiformant assessments are considered in the article. An algorithm for speech segmentation using momentary functions of third and fourth order is obtained. It is proposed to use digital filtering based on the sliding-average autoregressive model to suppress correlated noise. Estimates of variance in estimating the time boundaries of words for a number of proposed speech segmentation algorithms are obtained

ТЕОРІЯ КОМБІНАТОРНОЇ ОПТИМІЗАЦІЇ ТА ЗАДАЧІ ШТУЧНОГО ІНТЕЛЕКТУ

Багато задач штучного інтелекту моделюються з використанням теорії комбінаторної оптимізації. Це пов’язано з тим, що переважна їхня частина при знаходженні оптимального розв’язку потребує перебору варіантів. Перебірним же задачам властива комбінаторна природа. Для задач розпізнавання та сегментації мовленнєвого сигналу, клінічної діагностики наведено математичні моделі, які побудовані в рамках теорії комбінаторної оптимізації. Показано, що в задачах цього класу комбінаторні конфігурації можуть бути як аргументом цільової функції, так і вхідними даними.

Syllable Segmentation of Tamil Speech Signals Using Vowel Onset Point and Spectral Transition Measure

Segmentation plays vital role in speech recognition systems. An automatic segmentation of Tamil speech into syllable has been carried out using Vowel Onset Point (VOP) and Spectral Transition Measure (STM). VOP is a phonetic event used to identify the beginning point of the vowel in speech signals. Spectral Transition Measure is performed to find the significant spectral changes in speech utterances. The performance of the proposed syllable segmentation method is measured corresponding to manual segmentation and compared with the exiting syllable method using VOP and Vowel Offset Point (VOF). The result of the experiments shows the effectiveness of the proposed system.

Pitch segmentation of speech signals based on short-time energy waveform

In general, speech is constituted of quasi-repetitive patterns called pitches representing the speech fundamental period and tonal information of the voice. Extraction of pitch information that is crucial for many speech processing techniques, usually faces a noise problem and interference caused by high-order harmonic components. This paper introduces a novel, noise-robust method for determining speech fundamental frequency and pitch segmentation, based on a short-time energy waveform (SEW), defined as a moving average squared signal. When applying a moving average filter with a window size closed to the fundamental period, nearly repetitive patterns, with fewer ripples, synchronizing with actual pitches can clearly be observed in the SEW. The DC component in the SEW is removed using morphological top-hat and bottom-hat transforms. The fundamental frequency is determined as the frequency corresponding to the largest peak of the power spectrum of the DC-removed SEW. Finally, a time-domain window search is then performed to locate local extrema associated with pitches. Compared to traditional pitch detection techniques, the proposed technique yields pitch segmentation results with a higher rate of accuracy and greater noise robustness.

The speech signal segmentation algorithm using pitch synchronous analysis

Abstract Parameterization of the speech signal using the algorithms of analysis synchronized with the pitch frequency is discussed. Speech parameterization is performed by the average number of zero transitions function and the signal energy function. Parameterization results are used to segment the speech signal and to isolate the segments with stable spectral characteristics. Segmentation results can be used to generate a digital voice pattern of a person or be applied in the automatic speech recognition. Stages needed for continuous speech segmentation are described.