Three 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. For one of the methods feature extraction in C++ 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. Spectral envelope of speech signal and formant frequencies are displayed and stored in text files. Program user guide is presented in the work description. Investigation results of formant features for various speech sounds are presented in the appendices.

This thesis compares the speech quality of Code Excited Linear Predictor (CELP, Federal Standard 1016) speech coder with a new wavelet method to compress speech. The performances of both are compared by performing subjective listening tests. The test signals used are clean signals (i.e. with no background noise), speech signals with room noise and speech signals with artificial noise added. Results indicate that for clean signals and signals with predominantly voiced components the CELP standard performs better than the wavelet method but for signals with room noise the wavelet method performs much better than the CELP. For signals with artificial noise added, the results are mixed depending on the level of artificial noise added with CELP performing better for low level noise added signals and the wavelet method performing better for higher noise levels.

Thesis (M.S.)--Rochester Institute of Technology, 1989. / Includes bibliographical references (leaves 20-21).

Thesis (M.S.)--Rochester Institute of Technology, 1989. / Includes bibliographical references (leaves 81-85).

This study examined changes in speech production when distracting background audio is present. Forty typically speaking adults completed a repetitive sentence reading task in the presence of 5 different audio conditions (pink noise, movie dialogue, heated debate, classical music, and contemporary music) and a silent condition. Acoustic parameters measured during the study included vowel space area (VSA), vowel articulation index (VAI), formant transition extent, formant transition rate, and diphthong duration for /ɑɪ/ and /ɑʊ/. It was hypothesized that there would be significant increases in vowel space area and vowel articulation index as well as an increase in formant transition measures in the presence of background noise. There were statistically significant decreases in vowel space are and vowel articulation index in the presence of all noise conditions compared to the silent baseline condition. Results also demonstrated a significant decrease in F2 transition extent for both /ɑɪ/, and /ɑʊ/ diphthongs in all noise conditions except the pink noise condition when compared to the silent condition. These findings were contrary to what was originally hypothesized. It is possible that VAI and VSA decreased in the presence of background noise due to an increase in speaking rate. Formant transition measurements were consistent with the VAI and VSA results. More research is needed to accurately determine the acoustic changes a speaker makes in response to distracting background audio.

The diploma thesis Detection of Alcohol in Speech Signal first describes the effect of alcohol on the human body. The second part deals with ways to obtain parameters that describe the speech signal. The third part provides a brief overview of previous case studies and patents focused on the detection of alcohol in the speech signal. The fourth part presents the collected own database of voice recordings and developed software application for the analysis of intoxicated speech. The final part describes the measured changes in speech signal parameters that indicate alcohol intoxication.

No description available.

No description available.

20 amžiaus antrojoje pusėje ypač suaktyvėjo tyrimai kalbančiojo atpažinimo ir kalbos sintezavimo srityje. Jau nuo penktojo dešimtmečio vykdomi tyrimai siekiant sukurti sistemas galinčias atpažinti šnekamąją kalbą. Ypač svarbu šioje srityje yra kokybiškai atskirti kalbos signalus. Aštuntajame dešimtmetyje buvo sukurta eilė požymių išskyrimo metodų. Svarbesni iš jų yra melų skalės kepstras, suvokimu paremta tiesinė prognozė (perceptual linear prediction), delta kepstras ir kiti.[3] Naudojant šiuolaikinę kompiuterinę įrangą, signalų atskyrimo uždavinys gerokai supaprastėja, tačiau vis tiek išlieka labai sudėtingas. Kalbos sintezatorius yra kompiuterinė sistema, kuri gali atpažinti žmogaus balsą bet kokiame tekste. Sistema gali automatiškai sugeneruoti žmogaus balsą. Viena iš perspektyviausių balso technologijų panaudojimo sričių – įvairūs neįgaliems žmonėms skirti taikymai (akliems ir silpnaregiams, nevaikščiojantiems arba turintiems ribotas judėjimo galimybes). Balso technologijų panaudojimas dažnai yra esminis arba net vienintelis tokių žmonių integravimo į visuomenę būdas. Dar yra daugybė tokių sistemų panaudojimo sričių: • telefoninių ryšių centrai, automatiškai aptarnaujantys telefoninius pokalbius, atpažįstantys ir suprantantys, ką skambinantis sako; • automatinės transporto tvarkaraščių užklausimo sistemos; • automobilio mazgų valdymo žmogaus balsu priemonės; • nenutrūkstamos kalbos atpažinimo sistemos darbui teksto redaktoriais; Kalbos signalams analizuoti bei atskirti... [toliau žr. visą tekstą] / In 20th century speech recognition and synthesis became very important part of science. In last 50 years were a lot of researches in speech recognition. And for the moment there are many systems for speech recognition and synthesis for popular European languages, such as French, English, Germanic languages. One of the most important benefits of this is for disabled people to make their life more comfortable and adopt them to normal life, to create new interfaces and possibility to use personal computers for them. For Lithuanian language need researches, because of our language unique. An aim of research is a spectrum of Lithuanian consonants. Main method is linear prediction is used for finding formants. There are some main methods for speech signals analysis: linear prediction, Furier transformation, cepstral analysis. For linear prediction are several different algorithms. We used Burg algorithm for finding formants. In this research paper records of words were annotated and analyzed by PRAAT software. Formant movement obtained with same program. Obtained data of research was processed with MATLAB 6.5 software. All consonants were divided to groups, such as voiced and unvoiced, semivowels, plosives and fricatives. In our research was analyzed influence of vowels following after consonant. Obtained data is useful for increasing quality in speech recognition and synthesis. Paper includes: 1. Speech generation analysis. 2. Spectrum analysis methods. 3. Experiment methodology... [to full text]

A brief introduction into biometrics is described in this thesis leading to description and to design a solution of verification system using speech analysis. The designed system provides firstly basic signal processing, then vowel recognition in fluent Czech speech. For each found vowel, observed speech features are calculated. The created GUI application was tested on created speaker database and its efficiency is approximately 54 % for short testing utterances, and approx. 88 % for long testing utterances respectively.