Linear Prediction Coefficients Research Articles

Classification of Cough Sounds Using Spectrogram Methods and a Parallel-Stream One-Dimensional Deep Convolutional Neural Network

Currently, a subjective method is used to diagnose cough sounds, particularly wet and dry coughs, which can lead to incorrect diagnoses. In this study, novel emergent features were extracted using spectrogram methods and a parallel-stream one-dimensional (1D) deep convolutional neural network (DCNN) to classify cough sounds. The data of this study were obtained from two datasets. We employed the Mel spectrogram, chromagram constant- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> transform, Mel-frequency cepstral coefficient, constant- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> cepstral coefficient, and linear predictive code coefficient to conduct features analysis. The maximum, mean, variance, and standard deviation values of the original spectrogram as well as the maximum first and second derivatives of this spectrogram were extracted and fused to create a single-feature vector. We adopted two types of features: single features and combined features. Each design was restructured according to the magnitude of features with high discrimination power. A parallel-stream 1D-DCNN was developed for classifying cough sounds accurately. We compared the results obtained using the aforementioned network with those obtained using a single-stream 1D-DCNN. We found that the parallel-stream network outperformed the single-stream network for some feature sets. The developed network achieved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> 1 scores of 98.61% and 82.96% for the first and second datasets, respectively. The concatenation of layers at the flattening level resulted in an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> 1 score of 99.30% in dataset one. Moreover, layer merging strategies exhibited a better performance at the second convolutional layer level than at the flattening layer level in many cases.

DeepResGRU: Residual gated recurrent neural network-augmented Kalman filtering for speech enhancement and recognition

With the recent research developments, deep learning models are powerful alternatives for speech enhancement and recognition in many real-world applications. Although state-of-the-art models achieve phenomenal results in terms of the background noise reduction, but the challenge is to design robust models for improving the quality, intelligibility, and word error rate. We propose a novel residual connection-based Bidirectional Gated Recurrent Unit (BiGRU) augmented Kalman filtering model for speech enhancement and recognition. In the proposed model, clean speech and noise signals are modeled as autoregressive process and the parameters are composed of linear prediction coefficients (LPCs) and driving noise variances. Recurrent neural networks are trained to estimate the line spectrum frequencies (LSFs) whereas an optimization problem is solved to attain noise variances such that to minimize the divergence between the modeled and predicted autoregressive spectrums of the noise contaminated speech. Augmented Kalman filtering with the estimated parameters are applied to the noisy speech for background noise reduction such that to improve the speech quality, intelligibility, and word error rates. Bidirectional GRUs network is implemented which predicts parameters both in the future and past contexts of the input sequence and outperform in terms of modeling the long-term dependencies. A compensated phase spectra is used to recover the enhanced speech signals. The Kaldi toolkit is employed to train the automatic speech recognition (ASR) system in order to measure the word error rates (WERs). By using the LibriSpeech dataset, the proposed model improved the quality, intelligibility, and word error rates by 35.52%, 18.79%, and 19.13%, respectively under various noisy environments.

Automated sleep apnea detection in snoring signal using long short-term memory neural networks

Obstructive sleep apnea hypopnea syndrome (OSAHS) is a high incidence disease with serious hazard and potential danger. The polysomnography(PSG) has become the gold standard to diagnose OSAHS. However, the PSG is limited for household use because of its operational complexity, technical nature and high consumption.Currently, Microphone, as a non-contacting tensor, is an alternative method. And researchers devoted to analyze respiratory sound for detecting and evaluating OSAHS patients. In this paper, a classifier based on Long Short-Term Memory (LSTM) is proposed to identify the respiratory event-related snoring from simple snoring. Firstly, we collected the sleep sound of 33 patients and 10 normal people from the hospital. And 4780 abnormal snoring segments and 10,740 normal snoring segments were recorded by Mics. Then Mel-frequency cepstrum coefficients(MFCC), Mel Filter Banks (Fbanks), Short-time Energy and Linear Prediction Coefficient(LPC), representing the different characteristics of snoring, are extracted as characteristic features of snoring.At last, a multi-input model based on LSTM is designed, which can receive various audio features to synthesize information to identify snoring. Compared with single feature network processing, the use of multiple feature coefficients can identify the features of snoring at a fine-grained level. In the experiment, our method could classify respiratory event related snoring and normal snoring at accuracy 95.3%, and the accuracy of the three-category snore related to the severity of OSAHS can reach 81.6%. The recognition results can be used for the auxiliary diagnosis of OSAHS.

CAMNet: A controllable acoustic model for efficient, expressive, high-quality text-to-speech

Spoken language is becoming one of the key components of human–machine interaction, both to send information to the machine – e.g. voice control – and to receive from it – e.g. virtual assistants. In this scenario, text-to-speech (TTS) models have become an essential artificial intelligence capacity. Even though this interaction can be based on neutral style speech, generating speech with different styles, pitches and speaking rates may improve user experience. With this in view, this paper presents CAMNet, a controllable acoustic model for efficient, expressive, high-quality TTS. CAMNet is based on deep convolutional TTS (DCTTS), a state-of-art acoustic model which is efficient and produces neutral speech. DCTTS was first adapted to generate Bark cepstrum acoustic features in order to integrate well with the LPCNet (linear prediction coefficient) neural vocoder and to remove the reduction factor which demanded the presence of an upsampling network before the vocoder – i.e. the CAMNet output can be directly fed into LPCNet. Next, style transfer functionality was added by means of a novel characterisation of the prosodic information from the Bark cepstrum acoustic features and a new approach to inject this information into the convolutional layers. Finally, controllability is provided via a variational auto-encoder module which creates a smoothed disentangled latent space which allows interpolation and extrapolation of reference styles as well as independent and simultaneous control of two generative factors: pitch and speaking rate. Moreover, this controllability is implemented using a simple offset-based approach. To sum up, CAMNet is an efficient acoustic model which provides a simple but consistent controllability on coarse-grained expression, pitch and speaking rate while still providing high-quality synthesised speech.

Robustness and sensitivity metrics-based tuning of the augmented Kalman filter for single-channel speech enhancement

The inaccurate estimates of the speech and noise linear prediction coefficients (LPCs) introduce bias in augmented Kalman filter (AKF) gain, which impacts the quality and intelligibility of enhanced speech. Although current tuning methods offset the bias in AKF gain, particularly in colored noise conditions, they do not adequately address nonstationary noise conditions. This paper introduces a new tuning algorithm of the AKF gain for speech enhancement in real-life noise conditions. Due to this purpose, a speech presence probability (SPP) method first estimates the noise power spectral density (PSD) from each noisy speech frame to compute the noise LPC parameters. A whitening filter is constructed with the noise LPCs to pre-whiten each noisy speech frame prior to computing the speech LPC parameters. The AKF is then constructed with the estimated speech and noise LPC parameters. To achieve better noise reduction, the robustness metric is employed to dynamically offset the bias in AKF gain during speech absence of the noisy speech to that of the sensitivity metric during speech presence. The speech activity is obtained through adopting the speech and noise production model parameters. It is shown that the reduced-biased AKF gain achieved by the proposed tuning algorithm addresses speech enhancement in real-life noise conditions. Objective and subjective scores on the NOIZEUS corpus demonstrate that the proposed method produces enhanced speech with higher quality and intelligibility than the competing methods in real-life noise conditions for a wide range of signal-to-noise ratio (SNR) levels.

Robust children’s speech recognition in zero resource condition

User applications such as voice-based web search, online learning, and video gaming require an effective speech recognition module to take user commands. Nowadays, even children are frequently using such tools, especially for online learning and gaming. This has increased the demand for developing a noise-robust automatic speech recognition (ASR) system that can effectively transcribe children’s data under varied ambient conditions. However, automatic recognition of children’s speech is extremely challenging due to the insufficiency of data from child speakers in the majority of the languages across the world. Consequently, in this zero-resource condition, we are forced to decode children’s speech on systems trained using adults’ data. However, the acoustic mismatch between adults’ and children’s speech, such as differences in pitch, formant frequencies, and speaking-rates, leads to highly degraded recognition performance. To enhance the recognition rate under zero-resource conditions, we have explored the role of formant and duration-modification-based out-of-domain data augmentation in this paper. For that purpose, the formant frequencies of the adults’ speech data are upscaled using warping of linear predictive coding coefficients. On pooling original and formant modified adults’ speech data into training, the mismatch in formant locations is reduced leading to better recognition performance. Further improvement in recognition rate can be achieved by simultaneously modifying the duration as well as the formant frequencies of the training data. This case of out-of-domain data augmentation has also been studied in this work and found to yield added gains. In addition to data augmentation, a noise- and pitch-robust front-end acoustic feature extraction approach exploiting higher-order spectral analysis (simple and cross-bispectrum) is also proposed in this paper. The proposed features are noise-robust due to the inherent immunity of the bispectrum towards additive noises. An added advantage of bispectrum is reduced pitch sensitivity as demonstrated in this work. This, in turn, helps alleviate the aforementioned pitch-induced acoustic mismatch. The experimental evaluations presented in this paper demonstrate that the use of proposed acoustic features, as well as the out-of-domain data augmentation techniques, are highly suited for zero-resource children’s speech recognition tasks under clean and noisy conditions.

Effects of the Dynamic and Energy Based Feature Extraction on Hindi Speech Recognition

Background: Speech Recognition is the most effective and suitable way of communication. Extracted features play an important role in speech recognition. Previous research works for Hindi speech recognition lack detailed comparative analysis of the feature extraction methods using dynamic and energy parameters. Objective: The research work presents experimental work done to explore the effects of integrating dynamic coefficients and energy parameters with different feature extraction techniques on Connected word Hindi Speech recognition. As extracted features play a significant role in speech recognition, a comparative analysis is presented to show the effects of integration of dynamic and energy parameters to basic extracted features. Method: Speaker dependent system was proposed with monophones based five states Hidden Markov Model (HMM) using HTK Tool kit. Speech data set of connected words in Hindi was created. The feature extraction techniques such as Linear Predictive Coding Cepstral coefficients (LPCCs), Mel Frequency Cepstral Coefficients (MFCCs), and Perceptual Linear Prediction (PLPs) coefficients were applied integrating delta, delta2, and energy parameters to evaluate the performance of the proposed methodology for speaker dependent recognition. Results: Experimental results show that the system achieved the highest recognition word accuracy of 89.97% using PLP coefficients. The PLP coefficients achieved 4% increment in word accuracy than original MFCCs and a 16% increment in word accuracy than LPCCs. Adding energy parameters to original MFCCs increased word accuracy by 1.5% only while adding dynamic coefficients delta and double delta had no significant effect on speech recognition accuracy. Conclusion: Research findings reveal that PLP coefficients outperformed. Explorations reveal that the integration of energy parameters are better than original MFCCs. Investgations also reveal that adding energy parametres improved recognition score while adding delta and delta2 coefficients to basic features did not improve the recognition scores. Research findings could be used to enhance the performance of a speech recognition system by using a suitable feature extraction technique and combining the different feature extraction techniques. Further, investigations can be used to develop language resources for refining speech recognition. The work can be extended to develop a continuous Hindi speech recognition system.

Improved Estimation of Parkinsonian Vowel Quality through Acoustic Feature Assimilation.

This paper investigated the performance of a number of acoustic measures, both individually and in combination, in predicting the perceived quality of sustained vowels produced by people impaired with Parkinson's disease (PD). Sustained vowel recordings were collected from 51 PD patients before and after the administration of the Levodopa medication. Subjective ratings of the overall vowel quality were garnered using a visual analog scale. These ratings served to benchmark the effectiveness of the acoustic measures. Acoustic predictors of the perceived vowel quality included the harmonics-to-noise ratio (HNR), smoothed cepstral peak prominence (CPP), recurrence period density entropy (RPDE), Gammatone frequency cepstral coefficients (GFCCs), linear prediction (LP) coefficients and their variants, and modulation spectrogram features. Linear regression (LR) and support vector regression (SVR) models were employed to assimilate multiple features. Different feature dimensionality reduction methods were investigated to avoid model overfitting and enhance the prediction capabilities for the test dataset. Results showed that the RPDE measure performed the best among all individual features, while a regression model incorporating a subset of features produced the best overall correlation of 0.80 between the predicted and actual vowel quality ratings. This model may therefore serve as a surrogate for auditory-perceptual assessment of Parkinsonian vowel quality. Furthermore, the model may offer the clinician a tool to predict who may benefit from Levodopa medication in terms of enhanced voice quality.

Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement

Inaccurate estimates of the linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrade speech enhancement performance. The existing methods propose a tuning of the biased Kalman gain, particularly in stationary noise conditions. This paper introduces a tuning of the KF gain for speech enhancement in real-life noise conditions. First, we estimate noise from each noisy speech frame using a speech presence probability (SPP) method to compute the noise variance. Then, we construct a whitening filter (with its coefficients computed from the estimated noise) to pre-whiten each noisy speech frame prior to computing the speech LPC parameters. We then construct the KF with the estimated parameters, where the robustness metric offsets the bias in KF gain during speech absence of noisy speech to that of the sensitivity metric during speech presence to achieve better noise reduction. The noise variance and the speech model parameters are adopted as a speech activity detector. The reduced-biased Kalman gain enables the KF to minimize the noise effect significantly, yielding the enhanced speech. Objective and subjective scores on the NOIZEUS corpus demonstrate that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods.

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach.

Failure detection and diagnosis are of crucial importance for the reliable and safe operation of industrial equipment and systems, while gearbox failures are one of the main factors leading to long-term downtime. Condition-based maintenance addresses this issue using several expert systems for early failure diagnosis to avoid unplanned shutdowns. In this context, this paper provides a comparative study of two machine-learning-based approaches for gearbox failure diagnosis. The first uses linear predictive coefficients for signal processing and long short-term memory for learning, while the second is based on mel-frequency cepstral coefficients for signal processing, a convolutional neural network for feature extraction, and long short-term memory for classification. This comparative study proposes an improved predictive method using the early fusion technique of multisource sensing data. Using an experimental dataset, the proposals were tested, and their effectiveness was evaluated considering predictions based on statistical metrics.

Modeling and Reproducing Textile Sensor Noise: Implications for Textile-Compatible Signal Processing Algorithms.

Smart textiles provide an opportunity to simultaneously record various electrophysiological signals, e.g., ECG, from the human body in a non-invasive and continuous manner. Accurate processing of ECG signals recorded using textile sensors is challenging due to the very low signal-to-noise ratio (SNR). Signal processing algorithms that can extract ECG signals out of textile-based electrode recordings, despite low SNR are needed. Presently, there are no textile ECG datasets available to develop, test and validate these algorithms. In this paper we attempted to model textile ECG signals by adding the textile sensor noise to open access ECG signals. We employed the linear predictive coding method to model different features of this noise. By approximating the linear predictive coding residual signals using Kernel Density Estimation, an artificial textile ECG noise signal was generated by filtering the residual signal with the linear predictive coding coefficients. The synthetic textile sensor noise was added to the MIT-BIH Arrhythmia Database (MITDB), thus creating Textile-like ECG dataset consisting of 108 trials (30 min each). Furthermore, a Python code for generating textile-like ECG signals with variable SNR was also made available online. Finally, to provide a benchmark for the performance of R-peak detection algorithms on textile ECG, the five common R-peak detection algorithms: Pan & Tompkins, improved Pan & Tompkins (in Biosppy), Hamilton, Engelse, and Khamis, were tested on textile-like MITDB. This work provides an approach to generating noisy textile ECG signals, and facilitating the development, testing, and evaluation of signal processing algorithms for textile ECGs.

Dynamic mode decomposition: A feature extraction technique based hidden Markov model for detection of Mysticetes' vocalisations

The detection and classification of Mysticetes' vocalisations have evoked the attention of researchers over the years because of their relevance to the marine ecosystem. These vocalisations are gathered employing different passive acoustic monitoring techniques. The vocalisation datasets are accumulated over a period; thus, they are large and cannot be easily analysed manually. Consequently, efficient machine learning (ML) tools such as the hidden Markov models (HMMs) are used extensively to automatically detect and classify these huge vocalisation datasets. As with most ML tools, the detection efficiency of the HMMs depend on the adopted feature extraction technique. Feature extraction techniques such as the Mel-scale frequency cepstral coefficient (MFCC), linear predictive coefficient (LPC), and empirical mode decomposition (EMD) have been adopted with the HMMs to detect different Mysticetes' vocalisations. This article introduces the method of dynamic mode decomposition (DMD) as a feature extraction technique that can be adopted with the HMMs for the detection of Mysticetes' vocalisations. The DMD has emerged as a robust tool for analysing the dynamics of non-stationary and non-linear signals. It is a completely data-driven tool that decomposes a signal over a certain period of time into relevant modes. Here, these spatial-temporal modes are reconstructed mathematically to form reliable feature vectors of the decomposed vocalisation signals. The performance of the proposed DMD-HMM detection technique is demonstrated using the acoustic datasets of two different Mysticetes' vocalisations: Humpback whale songs and Inshore Bryde's whale short pulse calls. In both species, the proposed DMD-HMM exhibits superior sensitivity and false discovery rate performances as compared to the MFCC-HMM, LPC-HMM, and EMD-HMM detection methods. Likewise, this proposed DMD-HMM detection method can be extended to other Mysticetes' that produce characteristics sounds.

A comparison of Laryngeal effect in the dialects of Punjabi language

Human beings have their own speaking style which helped them in depicting their native language. The major reason behind variability in some language is due to varying dialect of the speakers. In the field of Automatic Speech Recognition (ASR), key challenge is to recognize and to generate an acoustic model which represents differences of redundant acoustic features. In this paper, an issue of dialect classification is perform on the basis of tonal aspects of laryngeal phoneme [h]. This is an empirical study of [h] sound words in four major dialects of Indian Punjabi language with two key parameters, namely F0 variation, and acoustic space, which are calculated using two formant frequencies: F1, and F2. The results are based on four different dialects which provide us some interesting hypotheses and are explored with self-created dataset. The speech analysis tool PRAAT features have been extracted and correlations are studied using Statistical Package for the Social Sciences (SPSS). Each variable has been compared with same variable of all other dialects. The results analysis showed that the fundamental frequency of these vowels are influenced distinctly in different dialectal conditions. Apart F1 and F2 have shown a significant correlation with each spoken dialect. Further work is extended through processing of acoustic information at feature level or by comparing the performance analysis using basic or hybrid Linear Predictive Cepstral Coefficients feature extraction methods. The result shows that the hybrid LPCC + F0 system achieved a Relative Improvement (R.I.) of 6.94% on Subspace Gaussian Mixture Model model in comparison to that of basic LPCC approach respectively.

EEG based direct speech BCI system using a fusion of SMRT and MFCC/LPCC features with ANN classifier

Brain computer interface (BCI) technology has a great deal of scientific interest with various application systems. An advancement that is increasingly relevant in the BCI is communicating with a speech in mind. Thus, the paper aims to develop a direct speech BCI (DS-BCI) system using short time-based features and an ANN classifier. Signal processing methods like Mel frequency cepstral coefficients (MFCC), Linear predictive cepstral coefficients (LPCC) and Sequency mapped real transform (SMRT) are utilized on a short time bases to extract base level features. Statistical parameters are then determined based on the ensemble average (EA) and time average (TA) to extract two reduced vectors in each method. Hybrid feature vectors like MLC, SMC and SLC are prepared by fusion of features from MFCC & LPCC, SMRT & MFCC and SMRT & LPCC, respectively, in both EA & TA analysis. Principal component analysis (PCA) is performed on hybrid feature vectors to derive uncorrelated components. The proposed method is evaluated on imagined EEG (EEG-i) & vocalized EEG (EEG-v) signals from the ‘Kara one’ database and presented classification accuracy of individual methods & hybrid methods. The results show that hybrid features SMC & SLC enhance the classification accuracy compared to the unique features. PCA analysis helps improve accuracy and reduce feature dimension. TA-based SMC features with PCA provide maximum accuracy as 77.37% and 62.52% for EEG-i and EEG-v signals, respectively. The proposed method outperforms the state-of-the-art algorithms discussed in the paper.

Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal.

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

Speaker Recognition Systems in the Last Decade – A Survey

Speaker Recognition Defined by the process of recognizing a person by his\her voice through specific features that extract from his\her voice signal. An Automatic Speaker recognition (ASP) is a biometric authentication system. In the last decade, many advances in the speaker recognition field have been attained, along with many techniques in feature extraction and modeling phases. In this paper, we present an overview of the most recent works in ASP technology. The study makes an effort to discuss several modeling ASP techniques like Gaussian Mixture Model GMM, Vector Quantization (VQ), and Clustering Algorithms. Also, several feature extraction techniques like Linear Predictive Coding (LPC) and Mel frequency cepstral coefficients (MFCC) are examined. Finally, as a result of this study, we found MFCC and GMM methods could be considered as the most successful techniques in the field of speaker recognition so far.

On the Speech Properties and Feature Extraction Methods in Speech Emotion Recognition.

Many speech emotion recognition systems have been designed using different features and classification methods. Still, there is a lack of knowledge and reasoning regarding the underlying speech characteristics and processing, i.e., how basic characteristics, methods, and settings affect the accuracy, to what extent, etc. This study is to extend physical perspective on speech emotion recognition by analyzing basic speech characteristics and modeling methods, e.g., time characteristics (segmentation, window types, and classification regions—lengths and overlaps), frequency ranges, frequency scales, processing of whole speech (spectrograms), vocal tract (filter banks, linear prediction coefficient (LPC) modeling), and excitation (inverse LPC filtering) signals, magnitude and phase manipulations, cepstral features, etc. In the evaluation phase the state-of-the-art classification method and rigorous statistical tests were applied, namely N-fold cross validation, paired t-test, rank, and Pearson correlations. The results revealed several settings in a 75% accuracy range (seven emotions). The most successful methods were based on vocal tract features using psychoacoustic filter banks covering the 0–8 kHz frequency range. Well scoring are also spectrograms carrying vocal tract and excitation information. It was found that even basic processing like pre-emphasis, segmentation, magnitude modifications, etc., can dramatically affect the results. Most findings are robust by exhibiting strong correlations across tested databases.

Feature Extraction Techniques with Analysis of Confusing Words for Speech Recognition in the Hindi Language

The research work presents experimental work to build a speaker-independent connected word Hindi speech recognition system using different feature extraction techniques with comparative analysis of confusing words. Comparative analysis of confusing words is essential to understand the reason for the speech recognition errors. Based on the error analysis, different feature extraction techniques, classification techniques, acoustic models, and pronunciation dictionaries can be selected to improve the speech recognition system's performance. Earlier studies for Hindi speech recognition lack detailed comparative analysis of confusing words for different feature extractions methods. As speaker-independent systems are developed for all, comparative analysis of confusing words is also presented for all feature extraction techniques. Speaker independent system was proposed with five states monophone based hidden Markov model (HMM) using HMM-based tool kit HTK. A Self-created data set of Hindi speech corpus has been used in the experiment. Feature extraction techniques such as linear predictive coding cepstral coefficients (LPCCs), mel frequency cepstral coefficients (MFCCs), and perceptual linear prediction coefficients (PLPs) were applied using delta, double delta, and energy parameters to evaluate the performance of the proposed methodology. The system was assessed by using different feature extraction techniques for speaker-independent mode. Research findings reveal that PLP coefficients show the highest recognition score, while LPCCs got the lowest recognition scores.Investigations also reveal that both PLP and MFCC coefficients are better than LPCC in speech recognition. Comparative analysis of confusing words shows that PLPs and MFCCs show fewer confusions than LPCCs and exhibit mostly the same pattern in the confusion analysis. Research outcomes also reveal that substitution errors are a significant cause of low recognition. It was also found that some words were recognized with individual feature extraction techniques only. Confusion analysis of the words indicates that words which have nasals, liquid, and fricative sound in first place exhibit more confusions. The investigation could improve speech recognition by choosing an appropriate feature extraction method and mixing the various feature extraction methods. The research outcomes can also be utilized to build linguistic resources for improving speech recognition. The results show that the developed recognition framework achieved the highest recognition word accuracy of 76.68% with PLPs for the speaker-independent model. The proposed system was also compared with existing similar work available.

Fog-Based Intelligent Machine Malfunction Monitoring System for Industry 4.0

There is an exponential increase in the use of Industrial Internet of Things (IIoT) devices for controlling and monitoring the machines in an automated manufacturing industry. Different temperature sensors, pressure sensors, audio sensors, and camera devices are used as IIoT devices for pipeline monitoring and machine operation control in the industrial environment. But, monitoring and identifying the machine malfunction in an industrial environment is a challenging task. In this article, we consider machines fault diagnosis based on their operating sound using the fog computing architecture in the industrial environment. The different computing units, such as industrial controller units or micro data center are used as the fog server in the industrial environment to analyze and classify the machine sounds as normal and abnormal. The linear prediction coefficients and Mel-frequency cepstral coefficients are extracted from the machine sound to develop and deploy supervised machine learning (ML) models on the fog server to monitor and identify the malfunctioning machines based on the operating sound. The experimental results show the performance of ML models for the machines sound recorded with different signal-to-noise ratio levels for normal and abnormal operations.

Steganography and Steganalysis in Voice over IP: A Review.

The rapid advance and popularization of VoIP (Voice over IP) has also brought security issues. VoIP-based secure voice communication has two sides: first, for legitimate users, the secret voice can be embedded in the carrier and transmitted safely in the channel to prevent privacy leakage and ensure data security; second, for illegal users, the use of VoIP Voice communication hides and transmits illegal information, leading to security incidents. Therefore, in recent years, steganography and steganography analysis based on VoIP have gradually become research hotspots in the field of information security. Steganography and steganalysis based on VoIP can be divided into two categories, depending on where the secret information is embedded: steganography and steganalysis based on voice payload or protocol. The former mainly regards voice payload as the carrier, and steganography or steganalysis is performed with respect to the payload. It can be subdivided into steganography and steganalysis based on FBC (fixed codebook), LPC (linear prediction coefficient), and ACB (adaptive codebook). The latter uses various protocols as the carrier and performs steganography or steganalysis with respect to some fields of the protocol header and the timing of the voice packet. It can be divided into steganography and steganalysis based on the network layer, the transport layer, and the application layer. Recent research results of steganography and steganalysis based on protocol and voice payload are classified in this paper, and the paper also summarizes their characteristics, advantages, and disadvantages. The development direction of future research is analyzed. Therefore, this research can provide good help and guidance for researchers in related fields.