MSAT-X: A technical introduction and status report

A technical introduction and status report for the Mobile Satellite Experiment (MSAT-X) program is presented. The concepts of a Mobile Satellite System (MSS) and its unique challenges are introduced. MSAT-X's role and objectives are delineated with focus on its achievements. An outline of MSS design philosophy is followed by a presentation and analysis of the MSAT-X results, which are cast in a broader context of an MSS. The current phase of MSAT-X has focused notably on the ground segment of MSS. The accomplishments in the four critical technology areas of vehicle antennas, modem and mobile terminal design, speech coding, and networking are presented. A concise evolutionary trace is incorporated in each area to elucidate the rationale leading to the current design choices. The findings in the area of propagation channel modeling are also summarized and their impact on system design discussed. To facilitate the assessment of the MSAT-X results, technology and subsystem recommendations are also included and integrated with a quantitative first-generation MSS design

Novel Pitch Detection Algorithm With Application to Speech Coding

This thesis introduces a novel method for accurate pitch detection and speech segmentation, named Multi-feature, Autocorrelation (ACR) and Wavelet Technique (MAWT). MAWT uses feature extraction, and ACR applied on Linear Predictive Coding (LPC) residuals, with a wavelet-based refinement step. MAWT opens the way for a unique approach to modeling: although speech is divided into segments, the success of voicing decisions is not crucial. Experiments demonstrate the superiority of MAWT in pitch period detection accuracy over existing methods, and illustrate its advantages for speech segmentation. These advantages are more pronounced for gain-varying and transitional speech, and under noisy conditions

Residual-excited linear predictive (RELP) vocoder system with TMS320C6711 DSK and vowel characterization

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