Linear Predictive Coding Technique Research Articles

Analysis of Formant Frequencies and Vowel Articulation in the Spoken Standard Nigerian English of Undergraduate Students

The study compares the first two formants' frequencies in the speech of male and female undergraduate speakers of Standard Nigerian English at the University of Lagos (UNILAG). The study examined eight monophthongs produced in the CVC context (hVd) by 40 males and 40 females, and analysed the vowel quality and phonetic vowel space used by the two gender groups using the Linear Predictive Coding and spectrogram techniques. The findings reveal that there are differences in vowel realisation and quality between male and female speakers, which affect their accents. Specifically, the male undergraduates exhibit compression in the phonetic vowel space while their female counterparts show expansion. This study contributes to the understanding of the vowel space in the speech of male and female speakers, and how these differences may influence speech accent.

Analysis of Rehabilitation Occupational Therapy Techniques Based on Instrumental Music Chinese Tonal Language Spectrogram Analysis.

This paper provides an in-depth analysis of timbre-speech spectrograms in instrumental music, designs a model analysis of rehabilitation occupational therapy techniques based on the analysis of timbre-speech spectrograms in instrumental music, and tests the models for comparison. Starting from the mechanism of human articulation, this paper models the process of human expression as a time-varying linear system consisting of excitation, vocal tract, and radiation models. The system's overall architecture is designed according to the characteristics of Chinese speech and everyday speech rehabilitation theory (HSL theory). The dual judgment of temporal threshold and short-time average energy realized the phonetic length training. Tone and clear tone training were achieved by linear predictive coding technique (LPC) and autocorrelation function. Using the DTW technique, isolated word speech recognition was achieved by extracting Mel-scale Frequency Cepstral Coefficients (MFCC) parameters of speech signals. The system designs corresponding training scenes for each training module according to the extracted speech parameters, combines the multimedia speech spectrogram motion situation with the speech parameters, and finally presents the training content as a speech spectrogram, and evaluates the training results through human-machine interaction to stimulate the interest of rehabilitation therapy and realize the speech rehabilitation training of patients. After analyzing the pre- and post-test data, it was found that the p-values of all three groups were <0.05, which was judged to be significantly different. Also, all subjects changed their behavioral data during the treatment. Therefore, it was concluded that the music therapy technique could improve the patients' active gaze communication ability, verbal command ability, and active question-answering ability after summarizing the data, i.e., the hypothesis of this experiment is valid. Therefore, it is believed that the technique of timbre-speech spectrogram analysis in instrumental music can achieve the effect of rehabilitation therapy to a certain extent.

DEVELOPMENT OF ACADEMIC ATTENDANCE SYSTEM USING VOICE VERIFICATION

Based on the fact that all Human Voices are different, where every individual’s voice contains unique characteristics that can be distinguished from others by using special analysis, voice verification can also be used for biometric recognition of individual. The aim of this project is to develop an automated attendance process captivating and student databases record maintaining using C++ programming and voice verification technique. The Input speech will undergo a series of voice recognition processes. The process starts with Feature extraction to obtain the attribute of voice. The features or specific characteristics for each voice are extracted by linear predictive coding technique (LPC) into feature vectors which later be used in speaker modeling. In speaker modeling stage the feature vectors of each speaker will be then processed by Gaussian Mixture Modeling (GMM) technique to generate speaker models and will be stored in a database. Verification process is done through template matching technique. Based on log-likelihood logic decision (template matching technique) with respect to the FAR (False Acceptance Rate) and FRR (False Rejection Rate), the identity of that speaker is accepted if the match is above the threshold. Once the verification result is accepted, the attendance in MySQL database for that speaker will be auto-updated.

Speech enhancement and encoding by combining SS-VAD and LPC

In this paper, the encoding of noisy and enhanced speech data is demonstrated. To encode and enhance the speech data under an uncontrolled environment, the linear predictive coding (LPC) and spectral subtraction with voice activity detection (SS-VAD) methods are studied individually. The noisy speech data is obtained by considering the amalgamation of the clean speech signal and noise model and it is encoded using the LPC technique. The LPC uses a lossy compression procedure to encode the speech data which converts the data rate from 64 to 2.4 Kbps. Due to reverberations and degradations in noisy speech data, the quality of encoded noisy speech data is very less. Therefore, an algorithm is proposed to enhance and encode the speech data by combining SS-VAD and LPC under degraded conditions. In the first step, the encoding of noisy speech data is done using LPC and its performance is evaluated using signal-to-ratio. The noisy speech data is given as input to the SS-VAD algorithm and the output of SS-VAD is given as input to the LPC encoder is followed in the second step. In the LPC encoder, the coefficients are extracted from the input speech data to design all-pole filters. The cross correlation process is also done for differentiating the voiced and unvoiced samples at the analysis step. The pitch information and extracted coefficients are used in the synthesis step. The experiments are conducted for different types of noisy speech data which are degraded by musical noise, F16 noise, factory noise, and car noise. The experimental results show that there is a significant improvement in the quality of enhanced encoded speech data obtained by the proposed method compared to encoded noisy speech data. The schematic representation of outputs of LPC and proposed combined SS-VAD and LPC waveforms are also given in this work.

HMM-based phoneme speech recognition system for the control and command of industrial robots

In recent years, the integration of human-robot interaction with speech recognition has gained a lot of pace in the manufacturing industries. Conventional methods to control the robots include semi-autonomous, fully-autonomous, and wired methods. Operating through a teaching pendant or a joystick is easy to implement but is not effective when the robot is deployed to perform complex repetitive tasks. Speech and touch are natural ways of communicating for humans and speech recognition, being the best option, is a heavily researched technology. In this study, we aim at developing a stable and robust speech recognition system to allow humans to communicate with machines (roboticarm) in a seamless manner. This paper investigates the potential of the linear predictive coding technique to develop a stable and robust HMM-based phoneme speech recognition system for applications in robotics. Our system is divided into three segments: a microphone array, a voice module, and a robotic arm with three degrees of freedom (DOF). To validate our approach, we performed experiments with simple and complex sentences for various robotic activities such as manipulating a cube and pick and place tasks. Moreover, we also analyzed the test results to rectify problems including accuracy and recognition score.

Efficient remote access system based on decoded and decompressed speech signals

This paper investigates the effect of both decoding and decompression on the Speaker Identification (SI) in a remote access system. The coding and compression processes are used for the communication purpose as a normal action taken for voice communication over Internet or mobile networks. In the proposed system, the speech signal is coded with the Linear Predictive Coding (LPC) technique. Also, the speech signal is compressed using two techniques. The first technique depends on decimation process to compress the signal. The signal can be recovered using inverse solutions. The inverse solutions include maximum entropy and regularized reconstruction. The second technique is the Compressive Sensing (CS) and the speech signal can be reconstructed using linear programming. The coded or compressed speech signal is transmitted into the receiver via a wireless communication channel. At the receiver, the received signal is decoded or decompressed, and then SI is performed on the decoded or decompressed speech signal. The performance of coding and compression techniques is evaluated using some metrics such as Perceptual Evaluation of Speech Quality (PESQ) and Dynamic Time Warping (DTW). The objective of SI is to achieve the security needed for the remote access system, and this security can be increased using coding and compression processes. In the SI system, the feature vectors are captured from different discrete transforms such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST), besides the time domain. The recognition rate for all transforms is computed to evaluate the performance of the SI system.

Classification of Multi Heart Diseases With Android Based Monitoring System

Electrocardiogram (ECG) examination via computer techniques that involve feature extraction, pre-processing and post-processing was implemented due to its significant advantages. Extracting ECG signal standard features that requires high processing operation level was the main focusing point for many studies. In this paper, up to 6 different ECG signal classes are accurately predicted in the absence of ECG feature extraction. The corner stone of the proposed technique in this paper is the Linear predictive coding (LPC) technique that regress and normalize the signal during the pre-processing phase. Prior to the feature extraction using Wavelet energy (WE), a direct Wavelet transform (DWT) is implemented that converted ECG signal to frequency domain. In addition, the dataset was divided into two parts , one for training and the other for testing purposes Which have been classified in this proposed algorithm using support vector machine (SVM). Moreover, using MIT AI2 Companion was developed by MIT Center for Mobile Learning, the classification result was shared to the patient mobile phone that can call the ambulance and send the location in case of serious emergency. Finally, the confusion matrix values are used to measure the proposed classification performance. For 6 different ECG classes, an accuracy ration of about 98.15% was recorded. This ratio became 100% for 3 ECG signal classes and decreases to 97.95% by increasing ECG signal to 7 classes.

Analysis of Template-Based Detection Algorithms for Inshore Bryde’s Whale Short Pulse Calls

Marine mammals use sound for communication and echolocation within their ecosystems. The detection of these sounds is an important aspect of signal processing, such that we can estimate the spatial position and direction of arrival of these mammals, and have an understanding of their ecology. Passive acoustic monitoring (PAM) is widely used to understand marine mammal movement and vocal repertoire. In PAM, datasets are accumulated over days, months or years. Thus, it is impracticable to manually analyse the datasets because it is very large. This motivated the development of automated sound detection techniques for marine mammals, which most often varies depending on the vocal duration, frequency range and call type. In this paper, continuous recordings of Bryde's whale (Balaenoptera edeni edeni) short pulse calls (<; 3.1s long) were collected on a weekly basis from December 2018 to April 2019 on sighting of the individual in a single site in the endmost South-West of South Africa. The sound, previously not documented off South Africa, was observed on visual confirmation of the presence of inshore Brydes's whale. In addition, the paper develops and analyses two automated template-based detection algorithms for this short pulse call, employing dynamic time warping (DTW) and linear predictive coding (LPC) techniques. These proposed template-based detectors are novel, as they have not being previously used in Bryde's whale sound detection in the literature. When applied to the continuous recordings of the short pulse calls, the DTW-based and LPC-based detection algorithms obtained a sensitivity of 96.04% and 97.14% respectively for high signal-to-noise ratio (about 10dB above the ambient sound). Otherwise, for low SNR, the DTW-based and LPC-based detection algorithms obtained a sensitivity of 94.98% and 96.00% respectively. These detection algorithms exhibit low computational time complexity and can be modified to analyse the movement of obscure but vocal marine species instead of manual identification.

Design and evaluation of a parametric model for cardiac sounds

Heart sound analysis plays an important role in the auscultative diagnosis process to detect the presence of cardiovascular diseases. In this paper we propose a novel parametric heart sound model that accurately represents normal and pathological cardiac audio signals, also known as phonocardiograms (PCG). The proposed model considers that the PCG signal is formed by the sum of two parts: one of them is deterministic and the other one is stochastic. The first part contains most of the acoustic energy. This part is modeled by the Matching Pursuit (MP) algorithm, which performs an analysis-synthesis procedure to represent the PCG signal as a linear combination of elementary waveforms. The second part, also called residual, is obtained after subtracting the deterministic signal from the original heart sound recording and can be accurately represented as an autoregressive process using the Linear Predictive Coding (LPC) technique. We evaluate the proposed heart sound model by performing subjective and objective tests using signals corresponding to different pathological cardiac sounds. The results of the objective evaluation show an average Percentage of Root-Mean-Square Difference of approximately 5% between the original heart sound and the reconstructed signal. For the subjective test we conducted a formal methodology for perceptual evaluation of audio quality with the assistance of medical experts. Statistical results of the subjective evaluation show that our model provides a highly accurate approximation of real heart sound signals. We are not aware of any previous heart sound model rigorously evaluated as our proposal.

Investigations of the Effect of Nonlinearly Generated Excitations on the Quality of the Synthesized Alaryngeal Speech

Objectives: To explore the use of ultrasonic waves based glottal excitation for reducing the distracting background noise present in the conventional artificial larynx. Methods/Analysis: The persons, whose larynx is removed, mostly use artificial larynx to generate the excitation, which produces background noise. Our hypothesis is that if the glottal excitation is generated by using two ultrasonic waves, the background noise may be reduced as the ultrasonic frequencies are inaudible. The audible excitation is generated within the vocal tract by nonlinear interaction of the external high frequencies focused around the larynx. Vocal tract parameters were extracted from the recorded normal and alaryngeal speech segments using Linear Predictive Coding (LPC) technique using analysis window of 20 ms with overlap of 10 ms and order of 500. Findings: Five types of excitations, sinusoid of the desired pitch frequency, sinusoid of high frequency, sinusoid of high frequency plus pitch, amplitude modulated high frequency and non-linearly generated excitation are used. The analysis of the results shows that non-linearly generated excitation may be used for reducing the background noise along with enhanced intelligibility and Mean Opinion Score (MOS) estimated for the cardinal vowels using non-linear excitation is 2.1, 4.0 and 3.7 with laryngeal vocal tract parameters, for the three cardinal vowels as compared to the MOS of 1.4, 1.0 and 2.3 with alaryngeal vocal tract parameters. Novelty: In the proposed technique, use of ultrasonic frequencies for generating the glottal excitation reduces the distracting audible background noise present in conventional techniques resulting in enhanced alaryngeal speech.

FORMANT ANALYSIS OF BANGLA VOWEL FOR AUTOMATIC SPEECH RECOGNITION

To provide new technological benefits to the mass people, nowadays, regional and local language recognition draws attention to the researchers. Similarly to other languages, Bangla speech recognition scheme is demandable. A formant is considered as the resonance frequency of vocal tract. Formant frequencies play an important role for the purpose of automatic speech recognition, due to its noise robust characteristics. In this paper, Bangla vowels are investigated to acquire formant frequencies and its corresponding bandwidth from continuous Bangla sentences, which are considered as potential parameters for wide voice applications. For the purpose of formant analysis, cepstrum based formant estimation and Linear Predictive Coding (LPC) techniques are used. In order to acquire formant characteristics, enrich continuous sentences and widely available Bangla language corpus namely “SHRUTI” is considered. Intensive experimentation is carried out to determine formant characteristics (frequency and bandwidth) of Bangla vowels for both male and female speakers. Finally, vowel recognition accuracy of Bangla language is reported considering first three formants..

Simulation and overall comparative evaluation of performance between different techniques for high band feature extraction based on artificial bandwidth extension of speech over proposed global system for mobile full rate narrow band coder

This paper addresses a novel approach to investigate, study and simulate computation of high band (HB) feature extraction based on linear predictive coding (LPC) and mel frequency cepstral coefficient (MFCC) techniques. Further, HB features are embedded into encoded bitstream of proposed global system for mobile (GSM) full rate (FR) 06.10 coder using joint source coding and data hiding before being transmitted to receiving terminal. At receiver, HB features are extracted to reproduce HB portion of speech and for the same different extension of excitation techniques are applied and their results evaluated in terms of quality (intelligibility and naturalness) and bandwidth. MATLAB based e-test bench is created for implementing the proposed artificial bandwidth extension (ABE) coder following series of simulations, that are carried out to discover and gain insight about the performance of it using subjective [mean opinion score (MOS)] and objective [perceptual evaluation of speech quality (PESQ)] analysis. The results obtained for both the analyses advocate that proposed ABE coder outperforms proposed GSM FR NB (legacy GSM FR) coder. While the fact remains that, compared to LPC based parameterizations over ABE coder, MFCC parameterization results in higher speech intelligibility which is evident from obtained slightly better PESQ and MOS scores.

Speech Coding Techniques

Abstract Speech coding has been major issue in the area of digital speech processing. Speech coding is the process of transforming the speech signal in a more compressed form, which can then be transmitted with few numbers of binary digits. It is not possible to access unlimited bandwidth of a channel each time we send a signal across it which leads to code and compress speech signals. Speech compression is applied in long distance communication, high-class speech storage, and message encryption. Speech coding is a lossy type of coding and hence the output signal does not exactly sound like the input. Speech coding techniques discussed here are Linear predictive coding, waveform coding, Code excited linear predictive coding, etc. Linear Predictive Coding and Code Excited Linear Predictive Coding techniques are studied with the help of MATLAB to check their performance measures like compression ratio and speech audible quality.

A novel approach for artificial bandwidth extension of speech signals by LPC technique over proposed GSM FR NB coder using high band feature extraction and various extension of excitation methods

This research addresses an issue of wide band (WB) speech transmission (having cut-off frequency $$\hbox {f}_{\mathrm{c}}=8$$fc=8 kHz) over standard narrow band (NB) communication link (supporting bandwidth of 300---3,400 Hz). A long transition time for technological up-gradation from NB to WB systems eventually lead to development of backward compatible techniques such as artificial bandwidth extension (ABE) which is capable of providing bandwidth of 50---7,000 Hz, in turn contributing toll quality recovered speech at receiving end. This paper investigates a novel approach to compute high band (HB) features using linear predictive coding (LPC) technique at transmitter from given input WB speech corpus. These encoded features are embedded into bitstream of proposed GSM Full Rate 06.10 NB speech coder using joint source coding and data hiding technique and then transmitted to receiver. At receiver, these HB features are extracted to reproduce HB recovered speech using watermark extraction algorithm and for the same different extension of excitation techniques have been adopted and implemented. An e-test bench is created to implement this proposed ABE coder in MATLAB and series of simulations are carried out using Subjective (mean opinion score--MOS) and Objective (perceptual evaluation of speech quality--PESQ) analysis. Obtained results for both analyses advocate performance improvement of proposed ABE coder over legacy GSM 06.10 FR NB coder for various extension of excitation techniques.

Arabic vowels recognition based on wavelet average framing linear prediction coding and neural network

In this work, an average framing linear prediction coding (AFLPC) technique for speaker-independent Arabic vowels recognition system was proposed. Usually, linear prediction coding (LPC) has been applied in many speech recognition applications, however, the combination of modified LPC termed AFLPC with wavelet transform (WT) is proposed in this study for vowel recognition. The investigation procedure was based on feature extraction and classification. In the stage of feature extraction, the distinguished resonance of vocal tract of Arabic vowel characteristics was extracted using the AFLPC technique. LPC order of 30 was found to be the best according to the system performance. In the phase of classification, probabilistic neural network (PNN) was applied because of its rapid response and ease in implementation. In practical investigation, performances of different wavelet transforms in conjunction with AFLPC were compared with one another. In addition, the capability analysis on the proposed system was examined by comparing with other systems proposed in latest literature. Referring to our experimental results, the PNN classifier could achieve a better recognition rate with discrete wavelet transform and AFLPC as a feature extraction method termed (LPCDWTF).

Average framing linear prediction coding with wavelet transform for text-independent speaker identification system

In this work, an average framing linear prediction coding (AFLPC) technique for text-independent speaker identification systems is presented. Conventionally, linear prediction coding (LPC) has been applied in speech recognition applications. However, in this study the combination of modified LPC with wavelet transform (WT), termed AFLPC, is proposed for speaker identification. The investigation procedure is based on feature extraction and voice classification. In the phase of feature extraction, the distinguished speaker’s vocal tract characteristics were extracted using the AFLPC technique. The size of a speaker’s feature vector can be optimized in term of an acceptable recognition rate by means of genetic algorithm (GA). Hence, an LPC order of 30 is found to be the best according to the system performance. In the phase of classification, probabilistic neural network (PNN) is applied because of its rapid response and ease in implementation. In the practical investigation, performances of different wavelet transforms in conjunction with AFLPC were compared with one another. In addition, the capability analysis on the proposed system was examined by comparing it with other systems proposed in literature. Consequently, the PNN classifier achieves a better recognition rate (97.36%) with the wavelet packet (WP) and AFLPC termed WPLPCF feature extraction method. It is also suggested to analyze the proposed system in additive white Gaussian noise (AWGN) and real noise environments; 58.56% for 0dB and 70.52% for 5dB. The recognition rates for the whole database of the Gaussian mixture model (GMM) reached the lowest value in case of small number of training samples.

Roles of spectral cues in Mandarin tone perception for sensorineural hearing-impaired patients

This study aimed to evaluate the role of spectral fine structure and envelope information in Mandarin tone perception for sensorineural hearing-impaired patients. The linear predictive coding technique was applied to synthesize 128 tokens having spectral fine structure from one tone of a monosyllabic word and spectral envelope from other tones with the same syllable. There after, the tone response performance was assessed using these 128 speech materials for 20 normal-hearing and 48 sensorineural hearing-impaired patients, including 20 with moderate, 20 with moderate to severe, and 8 with severe hearing loss. 99.7%, 97.7%, 96.3% and 83.0% of the mean tone responses were consistent with spectral fine structure for the normal hearing, moderate, moderate to severe, and severely hearing-impaired groups, respectively. No tone recognition responses were observed in consistent with spectral envelope cues. There was a significant difference in tone recognition among the normal hearing group and three hearing impaired groups (χ(2) = 59.2, P < 0.001). The negative correlation between the averages for pure-tone hearing thresholds at frequencies of 500, 1000, and 2000 Hz and tone responses that were in consistent with spectral fine structure for the hearing-impaired subjects (r = -0.643, P < 0.001). For both normal-hearing and hearing-impaired subjects, spectral fine structure plays an important role in tone recognition, and the impact of changing spectral envelope cues on tone recognition is negligible, when spectral fine structure cues are present. Sensorineural hearing impaired patients showed a progressive decrease in the recognition of lexical tones based on spectral fine structure cues, as their hearing loss becomes more severe.

Nalysis of Capnogram Using Linear Predictive Coding (LPC) to Differentiate Asthmatic Conditions

In this paper, an analysis of capnogram to differentiate asthmatic and non-asthmatic patients is presented by using linear predictive coding (LPC) technique. In the previous studies, manual study on capnogram signal has been conducted by several researchers. All previous researches show significant correlation between the capnogram and asthmatic patient. However all of them are just manual study conducted through the conventional time domain method. In this preliminary, a number of 8 LPC coefficients (α1- α8) for both asthmatic (CAP) and non-asthmatic patients’ capnogram (CNP) are extracted. Usefulness and performance of these coefficients to differentiate the asthmatic conditions by means of receiver operating characteristic (ROC) curve analysis are shown. Our preliminary results show that α3, α4, α5, and α6 can be used to distinguish the asthmatic conditions.

Autoregressive modeling of near-IR spectra and MLR to predict RON values of gasolines

A new calibration method that accurately predicts the Research Octane Number (RON) values of gasoline fractions, based on their infrared spectra, is presented. This model combines Linear Predictive Coding (LPC) and multiple linear regression (MLR) as an integrated estimation technique. Spectral information from the 4800–3520 cm −1 range was initially encoded into Linear Predictive (LP) coefficients, which were used as predictor variables in the MLR model against RON values. The model was trained and tested on an extensive data set (384 gasoline samples) and found to ensure prediction accuracy of 0.3 RON Root Mean Squared Error (RMSE). The LPC technique was found to be efficient in capturing spectral features of the entire range, related to the RON characteristics of the gasoline samples, without the need of any pretreatment on the experimental raw data. The small number of input variables in the regression model ensures a robust, easy-to-use and high accuracy prediction model.

Could formant frequencies of snore signals be an alternative means for the diagnosis of obstructive sleep apnea?

Objective To study the feasibility of using acoustic signatures in snore signals for the diagnosis of obstructive sleep apnea (OSA). Methods Snoring sounds of 30 apneic snorers (24 males; 6 females; apnea–hypopnea index, AHI = 46.9 ± 25.7 events/h) and 10 benign snorers (6 males; 4 females; AHI = 4.6 ± 3.4 events/h) were captured in a sleep laboratory. The recorded snore signals were preprocessed to remove noise, and subsequently, modeled using a linear predictive coding (LPC) technique. Formant frequencies (F1, F2, and F3) were extracted from the LPC spectrum for analysis. The accuracy of this approach was assessed using receiver operating characteristic curves and notched box plots. The relationship between AHI and F1 was further explored via regression analysis. Results Quantitative differences in formant frequencies between apneic and benign snores are found in same- or both-gender snorers. Apneic snores exhibit higher formant frequencies than benign snores, especially F1, which can be related to the pathology of OSA. This study yields a sensitivity of 88%, a specificity of 82%, and a threshold value of F1 = 470 Hz that best differentiate apneic snorers from benign snorers (both gender combined). Conclusion Acoustic signatures in snore signals carry information for OSA diagnosis, and snore-based analysis might potentially be a non-invasive and inexpensive diagnostic approach for mass screening of OSA.