A Microsoft Windows program is designed to implement a Multi-Language Identification system based on formants estimation and vector quantization classifier with n-Gram and HMM. LPC is used here as an effective method for formants feature extraction of the speakers, and a new method for distance measure of VQ is also proposed.

There are a lot of difficulties that have to be overcome in the speaker-independent (S.I.) phrase recognition system . And the feasibility of accurate ,real-time and robust system pose of the greatest challenges in the system. In this thesis ,the speaker-independent phase recognition system is based on Hidden Markov Model (HMM). HMM has been proved to be of great value in many applications, notably in speech recognition. HMM is a stochastic approach which characterizes many of the variability in speech signal. It applys the state-of-the-art approach to Automatic Speech Recognition .

A design of speech recognition system for Chinese names has been established in this thesis. By identifying surname first, that is an unique feature of the Chinese names, the classification accuracy and computational time of the system can be greatly improved. This research is primarily based on hidden Markov model (HMM), a technique that is widely used in speech recognition. HMM is a doubly stochastic process describing the ways of pronumciation by recording the state transitions according to the time-varing properties of the speech signal. The results of the HMM are compared with those of the segmental probability model (SPM) to figure out better option in recognizing base-syllables. Under the conditions of equal segments, SPM not only suits Mandarin base-syllable structure, but also achieves the goal of simplifying system since it does not need to find the best transformation of the utterance. A speaker-independent 3000 Chinese names recognition system has been implemented based on the Mandarin microphone database recorded in the laboratory environment.

The objective of this thesis is to reduce the training size for the Mandarin address inputting system and the Mandarin phrase recognition system. A set of two-word Mandarin phrases is developed by the balanced sieving and mixture training techniques. This greatly reduces the training data size for the systems. Hidden Markov model using both MFCC and LPCC features is proposed in this thesis. Speech-text alignment, frame overlapping and tone recognition are incorporated to increase the correct recognition rates. For the speaker-dependent case, any phrase in these two speech systems can be recognized within one second.

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.

Στα πλαίσια της διπλωματικής εργασίας αναπτύχθηκε μία εφαρμογή, η οποία προσδιορίζει τις τρεις πρώτες συχνότητες συντονισμού της φωνητικής κοιλότητας κατά τη διαδικασία της φώνησης φωνηέντων. Οι τρεις αυτές συχνότητες παρέχουν επαρκή πληροφορία για τον προσδιορισμό του φωνήεντου. Η φώνηση εξομοιώνεται με σήμα εισόδου το οποίο παρουσιάζει κορυφές σε αναμενόμενες περιοχές συχνοτήτων. Ο προσδιορισμός των συχνοτήτων συντονισμού στηρίζεται στη μέθοδο βραχύχρονης ανάλυσης Fourier. Η εφαρμογή αναπτύχθηκε σε περιβάλλον μVision της Keil, σε γλώσσα προγραμματισμού C, για τον μικροελεγκτή STM32F103RB της ST Microelectronics. / In the context of this thesis an application was developed, that is capable of estimating the first three formant frequencies (resonances of the vocal tract) in the event of voicing of vowels. These three frequencies provide us enough information to determine the vowel that is voiced. The human voice is being emulated by an input signal which has peaks in the anticipated frequency regions. The formant frequencies are being estimated based on the short-time Fourier analysis method. The application was developed in Keil μVision programming suite, in C programming language, for the STM32F103RB microcontroller by ST Microelectronics.

This thesis investigates the design and implementation strategies for a Korean speech recognition system. It utilizes the speech features of the common Korean mono-syllables as the major training and recognition methodology. A training database of 10 utterances per mono-syllable is established by applying Korean pronunciation rules. These 10 utterances are collected through reading 5 rounds of the same mono-syllables twice with different tones. The first pronounced pattern has high pitch of tone 1,while the second one has falling pitch of tone 4.Mel-frequency cepstral coefficients, linear predictive cepstrum coefficients, and hidden Markov model are used as the two feature models and the recognition model respectively. Under the Pentium 2.4 GHz personal computer and Ubuntu 9.04 operating system environment, a correct phrase recognition rate of 92.25% can be reached for a 4865 Korean phrase database. The average computation time for each phrase is about 1.5 seconds.

A speech recognition system for Chinese names based on Karhunen Loeve transform (KLT), MFCC, hidden Markov model (HMM) and Viterbi algorithm is proposed in this thesis. KLT is the optimal transform in minimum mean square error and maximal energy packing sense to reduce data. HMM is a stochastic approach which characterizes many of the variability in speech signal by recording the state transitions. For the speaker-dependent case, the correct identification rate can be achieved 93.97% within 3 seconds in the laboratory environment.

A Mandarin speech recognition system for addresses based on MFCC, hidden Markov model (HMM) and Viterbi algorithm is proposed in this thesis. HMM is a doubly stochastic process describing the ways of pronunciation by recording the state transitions according to the time-varing properties of the speech signal. In order to simplify the system design and reduce the computational cost, the mono-syllable structure information in Mandarin is used by incorporating both mono-syllable recognizor and HMM for our system. For the speaker-dependent case, Mandarin address inputting can be accomplished within 60 seconds and 98% correct identification rate can be achieved in the laboratory environment.

A system of Chinese resume by speech construction is developed by the use of a novel segmentation mechanism and the classical Hidden Markov Model. The recognition system is based on both mono-syllable HMM's and speech-text alignment schemes. Experimental results indicate that the amount of training materials used for feature extraction can be greatly reduced, and the text content of the recorded speech training data can be different from those of the recognition tasks as well. Each phrase in the resume can be identified within one second, that is approximately the same as the graduate did last year. Furthermore, the user interface of the resume system has been redesigned and polished by the GTK toolkit in order to enable event-driven X-window operations.