Linear Predictive Coding (LPC) has been used to compress and encode speech signals for digital transmission at a low bit rate. The Partial Correlation (PARCOR) parameter associated with LPC that represents a vocal tract model based on a lattice filter structure is considered for speech recognition. For the same purpose, the use of FIR coefficients and the frequency response of AR model were previously investigated. <p>In this thesis, we investigate the mechanics of the speech production process in human beings and discuss the place and manner of articulation for each of the major phoneme classes of American English. Then we characterize some typical vowel and consonant phonemes by using the eighth order PARCOR parameter associated with LPC.<p>This thesis explores a method to detect phonemes from a continuous stream of speech. The system being developed slides a time window of 16 ms and calculates PARCOR parameters continuously, feeding them to a phoneme classifier. The phoneme classifier is a supervised classifier that requires training. The training uses TIMIT speech database, which contains the recordings of 630 speakers of 8 major dialects of American English. The training data are grouped into the vowel group including phoneme [ae], [iy] and [uw] and the consonant group including [sh] and [f]. After the training, the decision rule is derived. We design two classifiers in this thesis, one is a vowel classifier and the other one is a consonant classifier, both of them use the maximum likelihood decision rule to classify unknown phonemes. <p>The results of classification of vowel and consonant in a one-syllable word are shown in the thesis. The correct classification rate is 65:22% for the vowel group. The correct classification rate is 93:51% for the consonant group. The results indicate that PARCOR parameters have the potential capability to characterize the phoneme.

Linear Predictive Coding (LPC) has been used to compress and encode speech signals for digital transmission at a low bit rate. The Partial Correlation (PARCOR) parameter associated with LPC that represents a vocal tract model based on a lattice filter structure is considered for speech recognition. For the same purpose, the use of FIR coefficients and the frequency response of AR model were previously investigated. <p>In this thesis, we investigate the mechanics of the speech production process in human beings and discuss the place and manner of articulation for each of the major phoneme classes of American English. Then we characterize some typical vowel and consonant phonemes by using the eighth order PARCOR parameter associated with LPC.<p>This thesis explores a method to detect phonemes from a continuous stream of speech. The system being developed slides a time window of 16 ms and calculates PARCOR parameters continuously, feeding them to a phoneme classifier. The phoneme classifier is a supervised classifier that requires training. The training uses TIMIT speech database, which contains the recordings of 630 speakers of 8 major dialects of American English. The training data are grouped into the vowel group including phoneme [ae], [iy] and [uw] and the consonant group including [sh] and [f]. After the training, the decision rule is derived. We design two classifiers in this thesis, one is a vowel classifier and the other one is a consonant classifier, both of them use the maximum likelihood decision rule to classify unknown phonemes. <p>The results of classification of vowel and consonant in a one-syllable word are shown in the thesis. The correct classification rate is 65:22% for the vowel group. The correct classification rate is 93:51% for the consonant group. The results indicate that PARCOR parameters have the potential capability to characterize the phoneme.

Counselor educators and counseling practitioners today reflect the future direction of the counseling profession; therefore, their opinions are important when discussing how professional counselors can reconcile the basic philosophies of humanistic counseling with the practical advantages and ethical and philosophical disadvantages that appear to be coexistent when discussing the diagnosis of clients and the Diagnostic and Statistical Manual of Mental Disorders IV-Text Revision (DSM). This study sought to provide a reflective and concise description of the current perceptions of licensed professional counselors in reference to their training, their practice, and their dispositions about diagnosis and utilization of the DSM despite its theoretical grounding in the medical model and its chronic problems with gender and cultural bias—all in direct opposition to counseling's humanistic, multicultural model of practice. Results of this study suggested that more training in DSM/diagnosis led to participants' higher perception of their ability to diagnose and utilize the DSM; however, participants' perceptions were split on whether or not training should include psychopharmacology. Results also suggested that LPCs most frequently occurring ethical dilemma in relation to diagnosis involved the reimbursement requirements of insurance/managed care companies; however, they strongly disagreed that diagnosing clients conflicted with their counseling professional identity. Participants strongly agreed that they were multiculturally competent; however, those participants who indicated that they diagnose using a multicultural or wellness perspective did not agree that the DSM does not adequately present disorders in such a way as to allow LPCs to diagnose culturally diverse and female clients accurately.

The area of speech recognition by machine is one of the most popular and complicated subjects in the current multimedia field. Linear predictive coding (LPC) is a useful technique for voice coding in speech analysis and synthesis. The first objective of this research was to establish a prototype of the residual-excited linear predictive (RELP) vocoder system in a real-time environment. Although its transmission rate is higher, the quality of synthesized speech of the RELP vocoder is superior to that of other vocoders. As well, it is rather simple and robust to implement. The RELP vocoder uses residual signals as excitation rather than periodic pulse or white noise. The RELP vocoder was implemented with Texas Instruments TMS320C6711 DSP starter kit (DSK) using C. Identifying vowel sounds is an important element in recognizing speech contents. The second objective of research was to explore a method of characterizing vowels by means of parameters extracted by the RELP vocoder, which was not known to have been used in speech recognition, previously. Five English vowels were chosen for the experimental sample. Utterances of individual vowel sounds and of the vowel sounds in one-syllable-words were recorded and saved as WAVE files. A large sample of 20-ms vowel segments was obtained from these utterances. The presented method utilized 20 samples of a segment's frequency response, taken equally in logarithmic scale, as a LPC frequency response vector. The average of each vowel's vectors was calculated. The Euclidian distances between the average vectors of the five vowels and an unknown vector were compared to classify the unknown vector into a certain vowel group. The results indicate that, when a vowel is uttered alone, the distance to its average vector is smaller than to the other vowels' average vectors. By examining a given vowel frequency response against all known vowels' average vectors, individually, one can determine to which vowel group the given vowel belongs. When a vowel is uttered with consonants, however, variances and covariances increase. In some cases, distinct differences may not be recognized among the distances to a vowel's own average vector and the distances to the other vowels' average vectors. Overall, the results of vowel characterization did indicate an ability of the RELP vocoder to identify and classify single vowel sounds.

The area of speech recognition by machine is one of the most popular and complicated subjects in the current multimedia field. Linear predictive coding (LPC) is a useful technique for voice coding in speech analysis and synthesis. The first objective of this research was to establish a prototype of the residual-excited linear predictive (RELP) vocoder system in a real-time environment. Although its transmission rate is higher, the quality of synthesized speech of the RELP vocoder is superior to that of other vocoders. As well, it is rather simple and robust to implement. The RELP vocoder uses residual signals as excitation rather than periodic pulse or white noise. The RELP vocoder was implemented with Texas Instruments TMS320C6711 DSP starter kit (DSK) using C. Identifying vowel sounds is an important element in recognizing speech contents. The second objective of research was to explore a method of characterizing vowels by means of parameters extracted by the RELP vocoder, which was not known to have been used in speech recognition, previously. Five English vowels were chosen for the experimental sample. Utterances of individual vowel sounds and of the vowel sounds in one-syllable-words were recorded and saved as WAVE files. A large sample of 20-ms vowel segments was obtained from these utterances. The presented method utilized 20 samples of a segment's frequency response, taken equally in logarithmic scale, as a LPC frequency response vector. The average of each vowel's vectors was calculated. The Euclidian distances between the average vectors of the five vowels and an unknown vector were compared to classify the unknown vector into a certain vowel group. The results indicate that, when a vowel is uttered alone, the distance to its average vector is smaller than to the other vowels' average vectors. By examining a given vowel frequency response against all known vowels' average vectors, individually, one can determine to which vowel group the given vowel belongs. When a vowel is uttered with consonants, however, variances and covariances increase. In some cases, distinct differences may not be recognized among the distances to a vowel's own average vector and the distances to the other vowels' average vectors. Overall, the results of vowel characterization did indicate an ability of the RELP vocoder to identify and classify single vowel sounds.

Kiekvienas, kuris kuria aparatūrinę įrangą, nori, tai atlikti kiek įmanoma greičiau ir už kuo mažesnius kaštus. Gaminys turi greitai patekti į rinką, nes egzistuojanti konkurencija yra labai didelė. Tai galima padaryti naudojant specialią programinę įrangą, vadinamus aukšto lygio sintezės įrankius. HLS įrankiai automatiškai generuoja HDL RTL aprašą bei padeda projektuotojams turėti visas galimas projekto architektūras. HLS įrankiai naudoja algoritminį C aprašą kaip įvesties duomenis. Čia atsiranda galimybė PĮ inžinieriams taip pat projektuoti aparatūrinę įrangą. Nors atrodo, jog HLS technologija yra labai gera, bet šie įrankiai šiandien plačiai nėra naudojami. Turi būti surasti problemu sprendimai. Šioms problemoms spręsti atliekamas eksperimentas. GSM LPC algoritmas rankiniu būdu perrašomas iš C kalbos aprašo į VHDL RTL. Eksperimentas paaiškina aukšto lygio sintezės problemas. Norint, kad HLS įrankiai būtų plačiai naudojami, aukšto abstrakcijos lygio C/C++ aprašas turi būti rašomas su atitinkamais apribojimais. Neturi būti naudajami rodyklės tipo kintamieji, rodyklės tipo kintamųjų aritmetikos, rekursijos, sudėtingų operacijų, dinaminio atminties rezervavimo. Projektuotojai turi mąstyti taip kaip aparatūrinė įranga. / Everyone who develops hardware wants to do this as fast as possible and for low costs. Time to the market must be shortened because the competition is very substantial. This can be done by using special development software called high level synthesis tools. HLS tools automatically generate HDL RTL code and helps developers to get all possible architectures of project. HLS tools use an algorithmic C code as input information. There is the possibility for software engineers to develop hardware too. It seems that HLS is very good technology, but HLS tools are not widely used today. It must be found the reasons of this problem and opportunities how this problem can be solved. An experiment is made by solving this problem. A GSM LPC algorithm is written by hand from C description to VHDL RTL. This experiment explains problems of high level synthesis. With the purpose HLS tools to use widely, high level of abstraction (C/C++) code must be written with restrictions. There must be no pointer variables and pointer arithmetic, no recursion, no difficult operations, no dynamic memory allocation. Engineers have to think like hardware.

Šiame darbe apžvelgėme pagrindinius kalbos atpažinimo požymius, naudojamus kalbos atpažinimo metoduose. Daugiausiai dėmesio skirta formantinių požymių išskyrimo metodams, o vienam iš metodų panaudota programinė įranga. Ji, remdamasi tiesinės prognozės modelio (LPC) parametrais, apskaičiuoja charakteringo polinomo kompleksines šaknis ir iš jų suranda kalbos signalo formančių dažnius. Darbe yra pateiktas aprašas kaip naudotis programa, prieduose įdėti atskirai ištartų žodžių kalbos garsų formantinių požymių tyrimo rezultatai. / In this work survey main speech recognition methods as well as features commonly used in speech recognition were discussed in the work. Main attention was paid to formant feature extraction methods. One of the methods feature extraction in software was developed. It is based on linear prediction coding (LPC) parameters and calculates complex roots of characteristic polynomial. These roots are used for formant frequencies determination. Program user guide is presented in the work description. Investigation results of formant features for various speech sounds are presented in the appendices.

A Mandarin keyword spotting system based on LPC, VQ, discrete-time HMM and Viterbi algorithm is proposed in the thesis. Joining with a dialogue system, this keyword spotting platform is further refined to a prototype of Taiwan Railway Natural Language Reservation System. In the reservation process, five questions: name and ID number, departure station, destination station, train type and number of tickets, and time schedule are asked by the computer-dialogue attendant. Following by the customer¡¦s speech confirmation, electronic tickets can be correctly issued and printed within 90 seconds in a laboratory environment.

Voice recognition technologies appeared in the period of general device miniaturization, when all technologies were commonly integrated into one lust. There is no space for buttons and displays anymore. To have a good system of Lithuanian language recognition, a number of throughout researches must be implemented. Only after selecting the most efficient speech recognition scheme, we can proceed to the development of software adapted to the contemporary time. The aim of this paper is to determine, how efficient speech recognition is possible using neuron networks. MFCC and LPC coefficients were chosen as the parameters characterizing the phonemes. The paper attempts at the determination of the coefficients, which lead to the most efficient recognition of phonemes. For testing, programs PRAAT and MatLab were used. After implementing a number of phoneme recognition experiments in the research work, the results were obtained, which lead to the following conclusions: 1. In case of using neuron network for the recognition of isolated sounds and characterizing the phonemes by MFCC or LPC coefficients, the possibility of recognition does not exceed 90 per cent. It is not enough for quality recognition of Lithuanian speech. 2. In case of using MFCC coefficients, separate phonemes are recognized better than using LPC coefficients. The difference is about 15 per cent. 3. The advantage of LPC coefficients in comparison with MFCC is the curve of recognition possibility, which is more even... [to full text]

MS is an autoimmune neurodegenerative disease that attacks myelin, a protective sheath that covers neurons within our bodies, which may lead to numbness, tremors, issues with vision, dizziness and more. When researching the efficacy of a therapeutic in neurodegenerative diseases such as multiple sclerosis, it is crucial that the in-vivo model selected for testing allows complete and accurate data collection. Several models attempt to replicate conditions of disease, in which myelin levels have been deliberately reduced in order to study its regrowth. These models (Cuprizone and LPC injection) can be further optimized by validating a new strain of mouse, NG2-creER / Rosa-Optopatch, which will essentially express GFP+ myelin. To validate this mouse line, the following goals were pursued: Confirm NG2+ pre-OLs express GFP in the spinal cord tissue and corpus callosum in our NG2-creER mouse, confirm that myelin formed from NG2+ pre-OLs that have matured into OLs also express GFP and characterize the GFP staining pattern along with other known myelin stains (MBP, Fluoromyelin Red), and in the long run, use the NG2-creER model in MS-related targets for drug candidates as a more efficient option than traditional methods such as electron microscopy (EM). Results show that the NG2-creER mouse was successful (in both CPZ and LPC models) in showing NG2+/GFP+ cells and that these GFP+ pre-OLs matured to form GFP+ myelin, as well as showing the capability of staining myelin at a younger age than other myelin stains. / 2022-06-17T00:00:00Z