An Analysis of Smart Clinical Trial Investigation Methods in Research Medicine Industries

Obuli, Saravanan; Udhayashankar, T.P.; Mageswari, R. Uma; Saritha, G.; Surendiran, J.; Vidhya, R.G.

doi:10.1109/iccpc55978.2022.10072260

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…The speech signal analysis for the vocal tract shape estimation can be broadly classified with the analysis and structured as shown in figure 5, which includes five different level of analysis such as speech signal analysis, pre-emphasis, hamming window technique, auto correlation method, parcor coefficients extraction and final estimation of the vocal tract shape [17].…”

Section: Linear Prediction and The Speech Modelmentioning

confidence: 99%

Autoregressive Based Vocal Tract Shape Modelling of Vowels in Speech Processing

Balakrishnan Sivakumar,

Praveen Kadakola Biligirirangaiah

2023

ARASET

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In the present research article, which concentrates on the estimation of the vocal tract shape for the five vowels of Indian English in the current growing interest in the field of speech processing, with practical limitation of the data that can be collected and analysed. Speakers without the degree of control on articulators cannot produce the desired data. Here, we aim to design an approximate phonatory model by improving the present approach for acoustic calculation with a fully aerodynamic simulation, explicitly accounting for the propagation of sound along the tract, generating patterns of movement of simulated vocal tract articulators, and specifying the temporal relations among dynamically defined gestures that lead to a time-varying vocal tract filter function, and an acoustic waveform. The principle of phonetic distinctiveness and formants frequency spread is one of the crucial parameters that are measured and compared on intra, inter-speaker in actual speaker identification & forensic applications.

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Section: Linear Prediction and The Speech Modelmentioning

confidence: 99%