In this thesis I will be concerned with linking the observed speechsignal to the configuration of articulators.Due to the potentially rapid motion of the articulators, the speechsignal can be highly non-stationary. The typical linear analysistechniques that assume quasi-stationarity may not have sufficienttime-frequency resolution to determine the place of articulation.I argue that the traditional low and high-level primitives of speechprocessing, frequency and phonemes, are inadequate and should bereplaced by a representation with three layers: 1. short pitch periodresonances and other spatio-temporal patterns 2. articulatorconfiguration trajectories 3. syllables. The patterns indicatearticulator configuration trajectories (how the tongue, jaws, etc. aremoving), which are interpreted as syllables and words.My patterns are an alternative to frequency. I use shorttime-domain features of the sound waveform, which can be extractedfrom each vowel pitch period pattern, to identify the positions of thearticulators with high reliability. These features are importantbecause by capitalizing on detailed measurements within a single pitchperiod, the rapid articulator movements can be tracked. No linearsignal processing approach can achieve the combination of sensitivityto short term changes and measurement accuracy resulting from thesenonlinear techniques.The measurements I use are neurophysiologically plausible: theauditory system could be using similar methods.I have demonstrated this approach by constructing a robust techniquefor categorizing the English voiced stops as the consonants B, D, or Gbased on the vocalic portions of their releases. The classificationrecognizes 93.5%, 81.8% and 86.1% of the b, d and gto ae transitions with false positive rates 2.9%, 8.7% and2.6% respectively.

This diploma thesis with estimation of pitch period of the human voice. The paper listed some of the methods for Estimation of pitch period and method for preprocessing and final processing of the signal after application of functions to determine the frequency of the pitch period in graphical user interface.

Este trabalho apresenta um estudo comparativo entre diferentes famílias de transformada Wavelet aplicadas à análise cepstral de sinais digitais de fala humana, com o objetivo específico de determinar o período de pitch dos mesmos e, ao final, propõe um algoritmo diferencial para realizar tal operação, levando-se em consideração aspectos importantes do ponto de vista computacional, tais como: desempenho, complexidade do algoritmo, plataforma utilizada, dentre outros. São apresentados também, os resultados obtidos através da implementação da nova técnica (baseada na transformada wavelet) em comparação com a abordagem tradicional (baseada na transformada de Fourier). A implementação da técnica foi testada em linguagem C++ padrão ANSI sob as plataformas Windows XP Professional SP3, Windows Vista Business SP1, Mac OSX Leopard e Linux Mandriva 10. / This work presents a comparative study between different family of wavelets applied on cepstral analysis of the digital speech human signal with specific objective for determining of pitch period of the same and in the end, proposes an differential algorithm to make such a difference operation take into consideration important aspects of computational point of view, such as: performance, algorithm complexity, used platform, among others. They are also present, the results obtained through of the technique implementation compared with the traditional approach. The technique implementation was tested in C++ language standard ANSI under the platform Windows XP Professional SP3 Edition, Windows Vista Business SP1, MacOSX Leopard and Linux Mandriva 10.

Este trabalho apresenta um estudo comparativo entre diferentes famílias de transformada Wavelet aplicadas à análise cepstral de sinais digitais de fala humana, com o objetivo específico de determinar o período de pitch dos mesmos e, ao final, propõe um algoritmo diferencial para realizar tal operação, levando-se em consideração aspectos importantes do ponto de vista computacional, tais como: desempenho, complexidade do algoritmo, plataforma utilizada, dentre outros. São apresentados também, os resultados obtidos através da implementação da nova técnica (baseada na transformada wavelet) em comparação com a abordagem tradicional (baseada na transformada de Fourier). A implementação da técnica foi testada em linguagem C++ padrão ANSI sob as plataformas Windows XP Professional SP3, Windows Vista Business SP1, Mac OSX Leopard e Linux Mandriva 10. / This work presents a comparative study between different family of wavelets applied on cepstral analysis of the digital speech human signal with specific objective for determining of pitch period of the same and in the end, proposes an differential algorithm to make such a difference operation take into consideration important aspects of computational point of view, such as: performance, algorithm complexity, used platform, among others. They are also present, the results obtained through of the technique implementation compared with the traditional approach. The technique implementation was tested in C++ language standard ANSI under the platform Windows XP Professional SP3 Edition, Windows Vista Business SP1, MacOSX Leopard and Linux Mandriva 10.