**3. System design**

84 Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

Estimating fundamental frequency has been a recurring issue in the area of digital signal processing. This is due to the fact that obtaining the time instants that define voice cycles is a very complex task. These cycles are used to obtain the fi frequency instants. Furthermore, it is vitally important to calculate these instants in the acoustic parameterization, as this is

Jitter [2] is a parameter representing variation of fundamental frequency, that is, the variations of pitch in each voice cycle. On the other hand, specialists also usually employ the shimmer parameter [2], which represents variation in width of voice cycle peaks. The voice produced through larynx modulation is able to almost constantly maintain peak width of voice periods. Therefore, an increase in shimmer value can be a symptom of voice disorder. Tables 1 and 2 present the various mathematical definitions of the jitter and shimmer

As previously mentioned, a number of authors have written several works on the detection of voice cycles [3,4] and there are also many highly detailed techniques to be found in the corresponding literature, such as estimators in the temporary domain (ratio of crosses per zero [5]), estimators of fundamental frequency [6,7], self-correlation methods (Yin estimators [8]), representation of the phase space [9], Cepstrums [10] and statistical methods [11, 12, 13]. Some of these directly define voice cycles [3], whereas others use numerical approximations [8] in order to obtain fundamental frequency values. In that respect, another step must be taken if we are to clearly identify the instants that

However, none of these works were tried out on oesophageal voices and, what is more, it can be stated without a shadow of a doubt that these algorithms are not suitable for voices of this kind. The software pack presented here is a tool designed for use by specialists in otolaryngology, and is specifically designed to obtain objective voice parameters with excellent precision. The tool contains a basic algorithm to calculate the acoustic parameters related to speech periodicity and serves as an aid for not only diagnosis and rehabilitation

It can be concluded that the tool is user-friendly and that ORL specialists can use it for measuring such objective parameters as pitch, jitter and shimmer, as well as for keeping

Speech signal processing plays an important role within the digital processing projects and investigations. Within this field, the esophageal voices are being objective of analysis and transformation [2],[3] but these have the limitation of measuring their quality only with subjective criteria as hearing tests. This is because an evaluation based on the calculation of objective parameters like pitch, jitter, shimmer or the harmonic to noise ratio HNR demands a high precision in the definition of the beginnings and ends of cycle

The oesophageal voice is generated using the air pass across the oesophagus but without the modulation possibility by the vocal fold because they have been removed due to,

the cornerstone of voice characterizations of this kind.

objective parameters.

define voice cycles.

but also for monitoring the patient.

patient records on these parameters.

**2.3 Software interface** 

in the voice signal.

The system design has been divided into two parts: the algorithm for improving the quality of oesophageal speech using wavelet transform and the user interface including the speech signal processing using that algorithm and the acoustic analysis of speech parameters.
