# Analysis of Phonetic Segments of Oesophageal Speech in People Following Total Laryngectomy
## 4 Discussion  
This paper proposes a method for analyzing oesophageal speech based on the analysis of phonetic segments determined by changes in loudness between successive sounds in the speech signal, the centre of which differs in loudness level from the nearest neighbor in the signal. As a result of the experiments, the most effective descriptors for parameterization of oesophageal speech were identified. These are the _MFCC_ coefficients, the local minimum and maximum indications, and the _ZCR_. Furthermore, it was found that the most important features of oesophageal speech occur mainly in the first phonetic segment. The table above presents the results of the classification of the phonetic segments (Seg. 1, Seg. 2 and Seg. 3) of the oesophageal and physiological speech. They provide a set of patterns that can be used to recognize the features of oesophageal speech--especially implemented during speech rehabilitation following laryngectomy surgery. To carry out the study, the WEKA package was used, from which the k-NN classifier and the cross-validation method for k = 10 were selected. Using the aforementioned classifier, all proposed featurevectors of features were tested. Since k-NN was used in each case of classification, it is possible to evaluate the effectiveness of the proposed feature vectors. It should be noted that the presented classification is one of the possible solutions, because it is plausible to combine the components of the features contained in the above feature vectors. However, other classification algorithms and rules (e.g., random forest or decision tables) can be applied, yet these will certainly provide different results. It was also shown that the effectiveness of the proposed feature vectors was different: beginning from good effectiveness to very poor effectiveness, and consequently did not provide any practical applications. Nonetheless, this knowledge is not without significance, as it allows us to exclude the sense of using ineffective descriptors for the classification of oesophageal speech in rehabilitated people. Examples of very poor classification are the cepstrum descriptors used for the second phonetic segment (general recognition 40,63%) and the distribution of four Ir descriptors used for the second phonetic segment (general recognition 43.75%). In general, it should be pointed out that good classification efficiency is provided by the _MFCC_ descriptors and the distribution of local maximum and minimum (general recognition is above 80%). The _MFCC_ descriptors also showed a good efficiency in the general recognition of oesophageal and physiological speech during whole- word analysis (without phonetic segmentation), which has been presented in detail [6]. Moreover, the distribution of the _ZCR_ descriptors for the first phonetic segment provides an overall classification score of almost 90%, being a very good result. It is worth noting that the analysis of the distribution of local minima and maxima for individual recognition of physiological speech reached 100% in three cases. The present study used a speech analysis approach (including syllable analysis of oesophageal speech) according to [28] and developed methods previously described by [29], which is in line with the main research trend. This allowed us to develop a group of tools based on coefficient analysis in the time domain, frequency domain, cepstrum, and _MFCC_ to study the physical features of oesophageal speech in people after total laryngectomies, and test them on the results of real healthy people, as well as the future development of this group of technologies to support early diagnosis and even prevention, and to compare our results with those of other Rothera research groups [28; 29; 30; 31; 32].