## 1 Introduction
In the article [11], the authors present the possibilities of speech parametrisation using popular time domain and spectrum domain descriptors, whose high efficiency in recognising the general speech signal was also demonstrated in the works [8,12,13]. The group of these popular MPEG-7 database descriptors includes, among others,  
1. ZCR (zero-crossing rate)--a measurement used to determine the rate of zero crossings (crossing of the OX axis). This is determined as the percentage of audio samples in a given fragment that change sign. The ZCR is defined by the following equation [14]: \[Z(i)=\frac{1}{2W_{L}}{\sum_{n=1}^{W_{L}}}|sgn[x_{i}(n)]-sgn[x_{i}(n-1)]|\] (1) where sgn(*) is the function, i.e., \[sgn[x_{i}(n)]=\left\{ \begin{array}{ll}1,&x_{i}(n)\geq 0,\\ -1,&x_{i}<0.\end{array} \right.\] (2)
2. Short-time energy (_STE_) is an audio descriptor from the MPEG-7 standard also used in speech classification. It describes the envelope of the signal. _STE_ is the sum of squares computed in the time domain over the length of the test frame of the signal. The _STE_ is expressed by the formula [14]: \[STE=\sum_{n=1}^{N}x^{2}(n)\] (3) where \(x(n)\)--the value of nth sample, \(n\)--index of the sample, \(N\)--signal length (total number of samples in the processing window). 3. The signal mean value (_SMV_) descriptor expresses the average value of the input speech signal. Its value is estimated in the tested frame of the audio signal. It is calculated by summing the values of all samples and dividing by \(N\). The _SMV_ is given by [14]: \[SMV=\frac{1}{N}{\sum_{n=1}^{N}x(n)}\] (4)  
The possibilities of ES parametrisation in time domain and spectrum domain using the above descriptors are also presented in the article [12]. These studies have shown the effectiveness of descriptors for describing ES. Speech parametrisation could be also realised in the cepstrum domain, which is mainly used to describe the laryngeal tone. In paper [15], the authors show the possibilities of improving automatic speech recognition (ASR) performance in noisy speech recognition cases by the using cepstrum domain. Cepstrum analysis and MFCC are also used to study ES, which is widely presented in [8,12]. In these articles, the effectiveness of recognising differences between ES and physiological speech based on cepstrum analysis is indicated. One of the most important parameters characterising the source of voiced speech is the fundamental frequency of the laryngeal tone (marked as F0), i.e., the fundamental frequency of the vocal fold vibrations, which are primarily a function of the mass, elasticity (stiffness), and the tension coefficient of the vocal cords [16]. The value of the F0 frequency depends, among other things, on the gender, age, and emotional state of the speaker. For men, the F0 frequency takes values from the 80-480 Hz range, and for women, from the 160-960 Hz range (speech and singing are taken into account). It should also be noted that F0 is usually not stationary, but changes constantly in a sentence or spoken word. As mentioned above, ES is characterised by a lower sound frequency than natural speech due to the characteristics of the pharyngeal wall--of course, this also applies to F0. This article presents an in-depth analysis of the F0 frequency of ES in people following total laryngectomy. The obtained results were compared with natural speech. For the purposes of the study, a common frequency range was found for laryngectomised and healthy people and the same spoken words (in Polish) were analysed--each as a separate pattern. The author of this article aims to find significant differences in the F0 frequencies of oesophageal speech and natural speech using words that are used during the rehabilitation of patients following total laryngectomy. This research is a continuation of the ES analysis published earlier, but this time the focus was on the analysis of a narrow aspect of speech, namely the fundamental frequency F0. This study was approved by the bioethics committee (KB 178/2020).