# SmHeSol (IoT-BC): Smart Healthcare Solution for Future Development Using Speech Feature Extraction Integration Approach with IoT and Blockchain

Authors: Shrikant Upadhyay; Mohit Kumar; Ashwani Kumar; Kayhan Zrar Ghafoor; S. Manoharan
Journal: Journal of Sensors
 (2022)

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## Abstract

Voice of any human plays an important role in communication and sharing information among each other. Through voice, internal behavior can be identified as to whether the person is happy or angry which is reflected. A person’s behavior is not exactly reflected by their face; variation in his/her voice reflects somehow their behavior as there will be variation in voice and variation in frequency and pitch. Feelings and natural behaviors are important features, and there are many biological aspects through which they can be identified. Therefore, in this paper, we consider a Hindi speech specimen of different groups to identify the person’s behavior and natural feelings under different acoustic conditions. Many research papers show emotion recognition based on neural networks with different models using speech signals to identify the present status of any patient, and it helps to build a way for a smart healthcare system. Enabling service in terms of Blockchain means the sufferer does not require communicating with complex and failed tasks for collecting information from various sources to send to their expert. Blockchain provides experts access to systems and enables entry to the dataset section. Patients have total control over their data, and they no longer require monitoring to keep their data managed and up to date. Also, manually coordinating with data is required for multiple visitors, which can be a very tedious one. In this paper, we focused on feature removal of speech using different extraction approaches which were used to know the quality or state of voice specimens and also understand which feature extraction plays a vital role in gaining a close state of speech. Internet of Things-based learning platforms are used to gather the voice sample, and also, a deep gaining method was followed to reach and achieve the best accuracy and identify the error rate which will help to gather close behavior and state of mind. Finally, a proposed model based on the Gaussian mixture model as a classifier was used for its spotting and attestation.


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## Body

## 1. Introduction

In the past few years, effort has been put into the domain of speech recognition. An attempt has been made for small, medium, and large vocabularies. But the optimum result was found to be valid if recorded data from the database for testing was performed with the same scenario against the training dataset. The speech signal system is affected mostly when background distortion rises [1].The addresser identification method involves automatically recognizing the person who is talking depending upon the individual’s speech wave information and its quality. The technique follows the talk of a person to recognize its original quality and provides private execution of any facility which includes dataset access, dialing through voice, medical emergency inputs, mail through voice, and secured and confidential data which also provides execution of computer information in remote areas and other many facilities associated with speech.Addressing through is complicated as it is associated with speech, and there will be various transformations that will arise at several stages which include interpretation, auditory, verbal, and lingual. Transformation differences are found in the verbal quality of talked speech; also, there is an addresser-related issue which involves the combination of corporeal dissimilarity inborn in the track of vocal and trained talking practice of any individual. In addresser identifications, all their dissimilarity is considered and used to differentiate two addressers, and it must be taken into account [2, 3]. Any behavior or mental status in terms of emotion identification using talked voice attracts more to detect the present status of a person. Through speech, various thoughts like behavior, emotions, and nature can be detected [4]. Smart healthcare development needs such an approach to make its structure strong using speech to realize the feeling of humans and reflect it to disclose sentiments [5, 6]. A smart health unit uses an IoT-based system that can solve such issues, and it is a challenging one and can provide solutions for problems arising in real time [7].


## 2. IoT-Based Smart Healthcare System

Embedded technology that contains a diversity of physical gadgets is an IoT used for detection and communication purposes. IoT provides technology to make daily life easy as its devices contain smart phones, detectors, actuators, etc. Development in the communication sector with the growth of mobile customers increases the strength of cloud-based technology for smart health systems and cities to enhance people’s life. A global ecological platform created by IoT where more than one device can be allocated and divide data with a cloud-based server completes their goal without any human interlinkage and support to generate new operations. The network requirements’ different communication protocols like wide-fidelity, Bluetooth, and 5G can be used as user demand to fulfill the satisfaction level in a defined communication range. The related and delivered data used for classification can be analyzed with proper planning and resolution-making where the created dataset from the raw sample is sent to the cloud network or any third party parts of the network system. IoT brings flexibility and luxury as it creates an easy path to handshake with various kinds of smart gadgets for observing different conditions and for the purpose of monitoring and communication [8–11].Encapsulated sensors and IoT gadgets help to measure different body parameters like oxygen level, blood pressure, body motion, and ECG signal in home and hospital premises. Emotions and behavior are also one of the human features which can be diagnosed as it is one of the important aspects of every human being. So, by capturing all the important features of speech and by reviewing the patient short- and long-term medical records, his/her treatment is possible at low cost and with minimum clinical cost [12]. Previous many research articles only focus on the emotion identification by collecting specimens of one group which was done that was not sufficient as the vocal track, resonance, and pitch are not considered as all these are important features of any human voice. So, such issues are taken into consideration for analysis. In medical analysis of voice, their variations, tone of voice, age factor, and quality are some of the physiological parameters that will also be taken into account for better performance [13–15]. In smart healthcare systems, the WSN, ultrahigh frequency, radio frequency identification, smart mobile, and GSM technologies were used for implementation [16]. The main goal of this research was to maximize early disease detection and minimize the diagnosis error and better prognosis [17]. Kumar et al. gave many solutions for detecting the object from the images using machine learning algorithms [18–21]. Figure 1 demonstrates the structure of the basic smart healthcare system [16].Figure 1
Structure of basic smart healthcare system [16].


## 3. Related Work

Feature recovery of any talk is important for any addresser. With the help of different feature recovery approaches, the pattern can be matched and modeling of signals is possible. Gathering of desired parameters which are crucial for those used by a person hearing structure is an immutable restriction which is crucial for the addresser and converter to maintain robustness in fluctuations while sending to the channel.Modeling of signals was done in this research paper which involves feature removal, shaping of spectra, transformation invariable, and its modeling. Variation in the spectrum with respect to time is one of the important issues that are also taken into consideration. The IoT platform is used to deliver the control inputs gathered by the authorized doctors or medical team, and the server detects the authenticated person’s voice and gives feedback again to authenticate one’s credential. It is found true that it will send the data for further processing. The IoT platform consists of hardware with its core component called raspberry PI. Tiwari et al. proposed a hybrid-cascaded framework for image reconstruction [22–25]. The basic structure consists of IoT gadgets, dataset, cell phone, cloud service, monitoring unit, etc. IoT gadgets in the form of wrist watch, wearable shirt with sensors, shoes, etc., interfaced with a cell phone which contains an app to read the inputs coming from different parts of the body. A smart cell phone contains a recognizable operating function and makes it possible to deliver information for its prediction, processing, and storage of data. The monitoring unit is directly interfaced with the healthcare unit, IoT gadgets, and activity done in hospitals. This unit deals with data, observation, transmission, and compression of large information to minimize the storage capacity. Figure 2 shows the various requirements of an effective smart healthcare system [22].Figure2
Requirements of smart healthcare system [22].The healthcare system when integrating speech with IoT enabled will be challenging for the system to take out the complete feature and its facts, and it is crucial to gather the complete information to compete with real-time devices. A quick response might be an essential requirement for the emergency need, and through speech, it is quite easy for the person to react and respond easily. Fact transmission, gathering of facts, and their compression are very important requirements for the IoT system. Keeping all these challenges, the integration and features that are removed must match the system at the maximum threshold to authenticate the person and identify its present mental status for quick response. Salamon and Bello [26] presented a second convolution neural community of three convolutions, accompanied by max-pooling and two hidden layers for environmental sound classification. For enhancing the accuracy of results, they applied the fact augmentation approach. Our approach and results show good accuracy in emotions and behavior predictions through the process of feature extraction and normalization, and predictions show better results. Castillo et al. [27] delivered clever technologies for detecting emotion in aged care. They in particular used cameras and frame sensors together with environmental elements such as tune, colour, and mild, inside the detection of older people’s feelings. We have advanced a live audio sentiment analysis device with the usage of audio sensors to locate human beings’ feelings via an audio class technique. Figure 3 shows a real-time speech identification system for emotions and behavior based on IoT and deep learning with speech emotions with its classification and workflow.Figure 3
Real-time speech feature extraction integration based on IoT and Blockchain. (b) Speech emotions with its classification and workflow [27].


## 4. Blockchain Role for Smart System

IoT brings new evolution to the healthcare sector with plenty of healthcare applications and health devices in markets. A bulk amount of information associated with healthcare are measured and delivered every hour inside or outside the healthcare unit for proper observations of patients. This huge amount of patient observation medical data with important information requires better management in terms of data security, privacy, and its availability. Patients’ medical status required complete monitoring by hospitals as well as doctors for better treatment while maintaining privacy and data security of sensitive data of patients to share them with medical institutes and leading hospitals for expert consultation for gathering better information about related cases. Accountability law and health insurance law enforcement and many other public agencies access medical information legally, and it is approximated that around 200 to 500 individuals may have the right to read the health records of any patient without any authentication and permission [28]. When information is distributed extensively and kept in multiple outbreaks, securing information is one of the more crucial issues. As per the Ponemon Institute, in the year 2016, about 112 million information related to medical was negotiated, and such breaks of data tampered with and attacks raised by 162% in the year 2017 [29, 30].


## 5. Methodology and Modeling

Modeling of signals can be done in the following ways. First is to shape the voice spectra; in the second step, the extraction of features will be done; in the third step, it involves modification of parameters involved with speech, and lastly, modeling will be done using analysis.


### 5.1. Shaping of Voice Spectra

Shaping of talked spectra requires two important functioning called filtering and its digitization [31]. In digitization, transformation of analog signals received in the form of sound waves can be converted into its digital form where filtering focuses on the crucial element, i.e., frequency ingredient present in signals. The shaping process is depicted in Figure 4. Digitization is one approach that is used to generate specimen data for the representation of talk signals with a high degree climb for signal intensity to noise intensity as possible. Conversion of signals over the final stage involves digital lookout cleaning and is accomplished using filters of impulse response given as
(1)Dempy=∑k=0nempbempky−k.Figure 4
Shaping of spectra with its operation.Normally, prior attention filtrations used are called coefficient filter which is of digital type:(2)Dempy=1+bempy−1.The classical value forbemp is −0.1‐−0.4, and this will help to strengthen the spectrum of signals nearly to about 20 dB decade.Prior attention filtration offers various advantages as naturally generated signals have an unfavorable negative slope nearly 20 dB/decade due to the physiology effect of the talked system [32–34]. And filtration turns down this natural slope prior to spectral investigation [35]. Also, aural is more delicate on the above 1-kilohertz locality of spectra which amplifies the nearby spectrum and helps the spectral algorithm construction and modeling of the crucial feature of the talked spectrum.


### 5.2. Removal of Feature

In speech identification where the addresser is independent, a superior offer is put down on feature removal that is somewhat undeviating to variation in the addresser which involves analysis of talked or spoken signal which is further classified into spectral investigation and temporal investigation.


### 5.3. Investigation of Decisive and Filter Margin

This investigation is basic and essential in talked processing. It is considered an unprocessed model of the starting phase of the natural process in the person hearing system.The filter margin as per “place theory” is the spot of higher shift along the primitive layer for incitation such as unmixed tones which is proportional to the frequency of logarithm of the tone [36].Human voice perception consists of a complex sound frequency within definite bandwidth of some minimal frequency which cannot be identified individually unless one of the quantities of this sound comes above the bandwidth which is known as decisive bandwidth [37].The approach with a combination of filter and decisive forms a theory where decisive and filter bank involves bandpass filtrate of linear aspect finite impulse which is arranged straightaway along the perceptual scale where bandwidths are considered to be equal to decisive for communicated centralized frequency [38]:
(3)Mel=13atan0.76f1000+3.51,Mel=3.5atanf275002+13atan0.76f1000.Therefore,(4)frequency=2595log101+f700.Then, decisive bandwidth can be written as(5)BWdec=25+251+1.4f100020.69.To make the group delay uniform for all delays, a linear phase filter is generally used and filter output signals are properly synchronized with respect to time. Then, the filter equation may be written as(6)Pi−12,Rin=∑βikrp+k,k=Pi−12,where βik indicates kth coefficient of ith decisive filter band.The analysis in form of output is a power value in vector for every supporting data. They are usually combined with parameters like average power to form vector measurement of signals. The bank of filter then attempts to decay the signal into a discrete level of spectrum specimens that must have similar information that is presented at a higher level for the auditory system. It is highly robust to the nose as this approach follows a linear processing rule [39].


### 5.4. Celestial Analysis

The celestial analysis is very crucial as it supplies the methodology to isolate the vocal activity and its stretching [2]. In speech generation for linear acoustic templates, the mixed spectrum of speech contains activity of filtered signals using time-assorted filter linear in characteristics for denoting the shape of vocal stretching as depicted in Figure 5.Figure 5
Model for speech generation.The talked signal is represented as(7)vn=hn·sn,where sn is the impulse response of the talked route and hn is the agitation signal.The representation of equation (7) in the frequency domain is
(8)Vf=Hf+SF,logVf=logHf=logS,VK=∑K=0K−1vvnexp−j2πKk,v^k=1K∑n=0K−1V^kexp−j2πKk.In talked identification, cepstrum verification is used for pitch and tracking of format. Samplev^k in the first 3 milliseconds is removed from the agitation.


### 5.5. Analysis of Mel-Scale Cepstral

Mel-scale analysis follows nonlinear quantity in terms of the frequency axis that uses cepstral. To get the mel cepstral, speech signalvk is recovered using filtration called window wn, and its discrete Fourier Vk is calculated.The height ofVk is then summed with a series of filter frequencies (mel) whose BW and center frequency matched with audible bandpass filters.In the next further step, the energy is evaluated in this loaded sequence. IfSlj is the response of the jth filter scale, the resulting energy for every talked frame at given time “n” will be
(9)Fmen,j=1Ci,whereMj and Uj denote higher and lower frequencies where every filter is nonzero and Ci is the power of filter to normalize as per BW varying to give equal power for a flat range.


## 5.1. Shaping of Voice Spectra

Shaping of talked spectra requires two important functioning called filtering and its digitization [31]. In digitization, transformation of analog signals received in the form of sound waves can be converted into its digital form where filtering focuses on the crucial element, i.e., frequency ingredient present in signals. The shaping process is depicted in Figure 4. Digitization is one approach that is used to generate specimen data for the representation of talk signals with a high degree climb for signal intensity to noise intensity as possible. Conversion of signals over the final stage involves digital lookout cleaning and is accomplished using filters of impulse response given as
(1)Dempy=∑k=0nempbempky−k.Figure 4
Shaping of spectra with its operation.Normally, prior attention filtrations used are called coefficient filter which is of digital type:(2)Dempy=1+bempy−1.The classical value forbemp is −0.1‐−0.4, and this will help to strengthen the spectrum of signals nearly to about 20 dB decade.Prior attention filtration offers various advantages as naturally generated signals have an unfavorable negative slope nearly 20 dB/decade due to the physiology effect of the talked system [32–34]. And filtration turns down this natural slope prior to spectral investigation [35]. Also, aural is more delicate on the above 1-kilohertz locality of spectra which amplifies the nearby spectrum and helps the spectral algorithm construction and modeling of the crucial feature of the talked spectrum.


## 5.2. Removal of Feature

In speech identification where the addresser is independent, a superior offer is put down on feature removal that is somewhat undeviating to variation in the addresser which involves analysis of talked or spoken signal which is further classified into spectral investigation and temporal investigation.


## 5.3. Investigation of Decisive and Filter Margin

This investigation is basic and essential in talked processing. It is considered an unprocessed model of the starting phase of the natural process in the person hearing system.The filter margin as per “place theory” is the spot of higher shift along the primitive layer for incitation such as unmixed tones which is proportional to the frequency of logarithm of the tone [36].Human voice perception consists of a complex sound frequency within definite bandwidth of some minimal frequency which cannot be identified individually unless one of the quantities of this sound comes above the bandwidth which is known as decisive bandwidth [37].The approach with a combination of filter and decisive forms a theory where decisive and filter bank involves bandpass filtrate of linear aspect finite impulse which is arranged straightaway along the perceptual scale where bandwidths are considered to be equal to decisive for communicated centralized frequency [38]:
(3)Mel=13atan0.76f1000+3.51,Mel=3.5atanf275002+13atan0.76f1000.Therefore,(4)frequency=2595log101+f700.Then, decisive bandwidth can be written as(5)BWdec=25+251+1.4f100020.69.To make the group delay uniform for all delays, a linear phase filter is generally used and filter output signals are properly synchronized with respect to time. Then, the filter equation may be written as(6)Pi−12,Rin=∑βikrp+k,k=Pi−12,where βik indicates kth coefficient of ith decisive filter band.The analysis in form of output is a power value in vector for every supporting data. They are usually combined with parameters like average power to form vector measurement of signals. The bank of filter then attempts to decay the signal into a discrete level of spectrum specimens that must have similar information that is presented at a higher level for the auditory system. It is highly robust to the nose as this approach follows a linear processing rule [39].


## 5.4. Celestial Analysis

The celestial analysis is very crucial as it supplies the methodology to isolate the vocal activity and its stretching [2]. In speech generation for linear acoustic templates, the mixed spectrum of speech contains activity of filtered signals using time-assorted filter linear in characteristics for denoting the shape of vocal stretching as depicted in Figure 5.Figure 5
Model for speech generation.The talked signal is represented as(7)vn=hn·sn,where sn is the impulse response of the talked route and hn is the agitation signal.The representation of equation (7) in the frequency domain is
(8)Vf=Hf+SF,logVf=logHf=logS,VK=∑K=0K−1vvnexp−j2πKk,v^k=1K∑n=0K−1V^kexp−j2πKk.In talked identification, cepstrum verification is used for pitch and tracking of format. Samplev^k in the first 3 milliseconds is removed from the agitation.


## 5.5. Analysis of Mel-Scale Cepstral

Mel-scale analysis follows nonlinear quantity in terms of the frequency axis that uses cepstral. To get the mel cepstral, speech signalvk is recovered using filtration called window wn, and its discrete Fourier Vk is calculated.The height ofVk is then summed with a series of filter frequencies (mel) whose BW and center frequency matched with audible bandpass filters.In the next further step, the energy is evaluated in this loaded sequence. IfSlj is the response of the jth filter scale, the resulting energy for every talked frame at given time “n” will be
(9)Fmen,j=1Ci,whereMj and Uj denote higher and lower frequencies where every filter is nonzero and Ci is the power of filter to normalize as per BW varying to give equal power for a flat range.


## 6. Voice Sample for Audio Analytics

The different voice samples of different ages and sexes expressing his/her feeling in Hindi text are collected using the above device samples collected using Cool Software, and samples are collected for different emotions and behavior performance in the form of spectrogram features as shown in Figure6. To increase classification, we have designed a green version of the usage of data normalization [9] and fact augmentation techniques in deep gaining knowledge of workflow for the classification of emotions and behaviors.Figure 6
Voice spectrum of different emotions using Hindi text.


## 7. Feature Extraction of Speech

When the incidents have unspecified random phenomena, then a stochastic prototype is used to exhibit the expression of probability density functions (pdfs). Here, the given frame corresponding to each observation vector is considered to be random, so each spoken word generated by the speaker is considered to be a random sequence of feature vectors. This model creates a perfect model for the sequence attending to the random sequence statistics such as its variance, mean, or probability distribution. The template of the feature vector probability distribution corresponding to a given speaker differs from the other speakers. Thus, the main objective of this prototype is to calculate the prospect point of a spoken phrase for every speaker model [40–42]. Hidden Markov and Gaussian mixture models are the illustration of stochastic structure complementary algorithms. Figure 7 represents the pattern matching system [40].Figure 7
Pattern matching system [40].The Hidden Markov Model (HMM) can be designed as a succession of feature vectors fully accurate where GMM (Gaussian mixture model) takes only a single feature vector corresponding to a single frame. HMM is efficient for text-dependent tasks, and GMM is good for text-independent tasks. Pattern matching is shown in Figure8. A dissimilar number of classes specified for each speech shape are first like voiced or unvoiced; then, it will use vector quantization in order to group feature vectors according to their similarity, and the last phase will use speech sound information. In the teaching phase, a pattern complementary algorithm will use whole training feature vectors to make speaker models. Per voice and per speaker for each model will be created [43]. The model will be called initial model architecture and reestimate and value them accordingly. The three feature extraction taken for behavior identification are Linear Prediction Cepstral Coefficients (LPCC), Linear Predictive Coding Analysis (LPC), and Mel-Scale Cepstrum Coefficient (MFCC).Figure 8
Proposed model for speech validation and identification.


## 8. Propose Model with Integration of Feature Integration

For the second model, we prepare different subsystems having their own feature set and acoustic phonetic modeling using a microphone for acoustic to phonetic realization including universal background GMM as one of the classifiers of this model and try to analyze the efficiency and error rate for the same text shown as depicted in Figure8.We also prepare different subsystems having their own feature set and acoustic modeling using a microphone for acoustic to phonetic realizations of voice signals. Context-dependent phonemes are used as a basic unit of phonetics in this modeling structure. Subsystems generate hypotheses independent using different mechanisms but are compatible with each other. Now, keeping combinations of the proposed combinations of features, we try to analyze the performance in terms of efficiency and error rate of Hindi text. Figures9 and 10 are collections of specimens of talked and specimen of voice samples, respectively.Figure 9
Collection of specimen of talked (sample taken during experiment).Figure 10
Specimen of voice samples (sample taken during experiment).The technical aspect of the tracking mechanism depends upon both IoT devices and the interfacing unit that helps to send and receive the information that consists of IoT gadgets, cloud server, datasets, and processing unit. The tracking mechanism is followed by the IoT sensor which is used to send the generated or gathered data to the wireless network using gateway service which is directly connected with the cloud server. This server directly transfers the information to the remote intelligent hospitals and the healthcare section (concern doctors) to share various information and prepare them to take some effective steps against any emergency. Figure11 shows the proposed self-tracking using IoT enabled using speech feature for the smart facility in hospitals [34].Figure 11
Proposed self-tracking using IoT enabled using speech feature for smart facility in hospital [34].


## 9. Results and Discussion

The dataset has recordings of 20 males and 20 females, and these recordings have extraordinary emotion categories, i.e., calm, satisfied, unhappy, indignant, worried, wonder, disgust, and impartial. We took into consideration seven emotion classes for experimentation. We decided to work with men and women facts one at a time.The variation in the spectrum for different emotions and behavior is shown in Figures12(a) and 12(b). The facts have been validated using the specimen of masculine and lassie of different age groups to evaluate the exact variations in their tone and get the various ingredients to analyze the performance of quality in terms of pitch, variation, clarity, etc. The above self-tracking model will help to access the remote information coming from rural areas and provide better precision in terms of identification of speech for better response and support healthcare to provide urgent help to people. The cloud acts as a server for sharing the facts to the nearest local healthcare unit to reach the people who require emergency support and grant medical facility for better health.Figure 12
Variations in frequency for different talks with emotions (a). Variations in frequency for different talks with emotions (b).
(a)(b)The various Hindi spoken digits considered for the analysis are represented in Table1, and the sound file for different emotions with their original and accelerated file of different age groups is represented in Table 2.Table 1
Hindi spoken digit.
रोतेहुएबोलना (cry out)गुस्साकबोलना (to speak angrily)आईलवयू/i love youदर्दभरीआवाज/painful soundजोरसेबोलना/blurtचिल्लाकरबोलना/shout outघबराहटकेबादबोलना/speaking after panicहँसतेहुएकहना/laughing to sayमधुरगाना/singing sweetlyबिनासोचेसमझेबोलना/speaking without thinkingTable 2
Sound file.
Serial no.Name & sexTypes of emotions## file (original)# accelerated file1Anuj_56Fरोतेहुएबोलना/cry out9612482Sandeep_65Mआईलवयू/i love you9612483Rajim_42Fजोरसेबोलना/blurt9612483Sarojni_43Fघबराहटकेबादबोलना/speaking after panic9612485Narang_39Mमधुरगाना/singing sweetly961248Total4806,240The speaker identification performance for different feature extraction is represented in Figure13 where the performance of MFCC is found to be efficient compared to LPCC and LPC.Figure 13
Behavior prediction using feature extraction.The speaker verification performance for different feature extraction is represented in Figure14 where the performance of MFCC is again observed to be efficient compared to LPCC and LPC.Figure 14
Efficiency for speaker verification (using different feature extraction).


### 9.1. Experiments with Combined Feature Extraction in Noise-Free Environment Using First Model

Here, the combination of three feature extraction techniques, i.e., LPC+LPCC, LPCC+MFCC, and MFCC+LPC, has been analyzed for the same database of 50 speakers in ideal conditions. Integration of feature removal tries to analyze the various qualities of talked speech and is used to gather the collection of information having variations in speech with his/her feeling, and it covers its emotions also. Integrating the quality of feature with IoT is really a challenging one as the system requires continuous analysis in real time for monitoring and updating the status. The integration of feature extraction for different Hindi dialects and its performance is represented in Table3.Table 3
Efficiency rate in % of combined feature extraction.
Hindi dialectsLPC+LPCC (%)LPCC+MFCC (%)MFCC+LPC (%)HAA91.2197.7698.98TUM92.8996.5498.65JAO92.1295.9997.22NAHI94.9896.3997.52KAHAN91.6794.7898.99RAAT89.2295.5498.54JEENA90.2995.6597.98MAUSAM95.8793.9299.10JAISAY92.6592.3297.87KYA HUA92.1093.9998.22The prediction of male and female talk throw performance using three faithful normalization approaches, i.e., EBU, peak, and RMS, which show the best accuracy for the female voice is depicted in Figure15.Figure 15
Prediction of male and female talk for different throws.After integrating the considered feature extraction technique, the combination of MFCC+LPC is observed to be quite efficient; this is found to be nearly about 98.92% as shown in Figure16.Figure 16
Efficiency rate in low acoustic condition.In this experiment, the second model using combined feature extraction was performed in which the performance of MFCC+LPC has been found more efficient, i.e., 98.22%, compared to that of LPCC+MFCC and LPC+LPCC, i.e., 93.99% and 92.10%, under such scenario.


## 9.1. Experiments with Combined Feature Extraction in Noise-Free Environment Using First Model

Here, the combination of three feature extraction techniques, i.e., LPC+LPCC, LPCC+MFCC, and MFCC+LPC, has been analyzed for the same database of 50 speakers in ideal conditions. Integration of feature removal tries to analyze the various qualities of talked speech and is used to gather the collection of information having variations in speech with his/her feeling, and it covers its emotions also. Integrating the quality of feature with IoT is really a challenging one as the system requires continuous analysis in real time for monitoring and updating the status. The integration of feature extraction for different Hindi dialects and its performance is represented in Table3.Table 3
Efficiency rate in % of combined feature extraction.
Hindi dialectsLPC+LPCC (%)LPCC+MFCC (%)MFCC+LPC (%)HAA91.2197.7698.98TUM92.8996.5498.65JAO92.1295.9997.22NAHI94.9896.3997.52KAHAN91.6794.7898.99RAAT89.2295.5498.54JEENA90.2995.6597.98MAUSAM95.8793.9299.10JAISAY92.6592.3297.87KYA HUA92.1093.9998.22The prediction of male and female talk throw performance using three faithful normalization approaches, i.e., EBU, peak, and RMS, which show the best accuracy for the female voice is depicted in Figure15.Figure 15
Prediction of male and female talk for different throws.After integrating the considered feature extraction technique, the combination of MFCC+LPC is observed to be quite efficient; this is found to be nearly about 98.92% as shown in Figure16.Figure 16
Efficiency rate in low acoustic condition.In this experiment, the second model using combined feature extraction was performed in which the performance of MFCC+LPC has been found more efficient, i.e., 98.22%, compared to that of LPCC+MFCC and LPC+LPCC, i.e., 93.99% and 92.10%, under such scenario.


## 10. Conclusion and Future Scope

From the above result, we conclude that the prototype for real statistics when executed for feminine talk voice was found to have greater precision with 98% contrast to masculine talk/audio voice and found to be maximum at 95%. We also noticed that our model was performing excellent, but some audio was found to have low accuracy due to noise, time, and compression. Hence, we normalize the audio using promising approaches like RMS, peak, EBU, and accelerated file where the accuracy of the male voice is found to be 92% whereas for female, it was about 98%. The behavior of different feature extraction was analyzed, and three important features LPC, MFCC, and LPC are simulated with the standard and classical model of speech identification and verification following all the standards and regulations of the models. And it was observed that the feature extraction behavior of MFCC is quite efficient, i.e., approx 97%, as compared to LPC and LPCC in acoustic conditions. Integration of feature extraction gives rise to the efficiency rate of 98% which is quite impressive for precision and reflects better to implement for the system. In deep learning, challenging tasks for tutoring prototypes with small fact and observed fact data for training prototype increasing data samples may increase accuracy and real-time prediction. For augmentation (data), we follow pitch shifting, time stretching, and range compression without any external effect. We have applied 60-40% ratio tutoring and validation used to take out spectrogram of speech in terms of features which increase the number of samples in terms of audio and model representing better accuracy for female voice, i.e., 98% and 97% for male voice. The accuracy prediction for behaviors was also done for male and female voices where female voice was found to be more accurate compared to male voice due to pitch stretching in male voice. This can be adopted in the smart healthcare system to identify the emotions and behavior of any patient in the future. Blockchain provides access to facts given by the source and is suitable for speech as it extracts all its behavior of speech and makes it secure for future real-time applications.



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