**8. Artificial neural networks analysis**

The Supervised Self-Organizing Map (supervised SOM, or SSOM) is based on clustering. These maps and their subsequent visualization help to monitor the progress of trends and magnitude of the degree of impairment. The algorithm of the SSOM represents a very effective classification approach, but it is only effective for well-known input data or for well-known classes of input data. ANNs were selected because of their notable robustness and strong ability to perform data visualization; hence, they can also process less qualitative signals.

#### **8.1. Description of method**

This study included 72 controls and 14 cases. The goal was to categorize the subjects into two classes, controls vs. cases. We obtained the results from the speech analysis via vowel mapping with speech from cases by SSOM.

SSOM classification: SSOM was formed by a two-dimensional map with 24 × 24 units. The type of map had a hexagonal grid with a random initialization of the vectors. The following two stages of training were used:


**Figure 10.** The results of the vowel classification by using SSOM maps. Part A represents a 2-D map, and part B represents a U-matrix.

#### **8.2. Evaluation and results**

(a) A parent or someone else reads the text, and the child repeats the same text (see **Table 1**).

(c) The text box for the correction of spoken words is pre-filled. The wrong form of a spoken word needs to be replaced, for example, changing the wrong form of the word *"nos"* (in

(d) The final evaluation, test results and recording of the child's speech can be sent to a speech

The application allows for viewing of a list of therapeutic consulting rooms, which are associated with the professional association of clinical speech pathologists in the Czech Republic (AKL CR). Here, it is possible to identify concrete speech and find a language pathologist who can evaluate the results via email. This email with the test evaluation also contains information about the test, the obtained errors, a recommendation based on the final score and an audio recording of test. Audio recordings can be especially beneficial in a comprehensive report on the possible language and speech difficulties of a child. The SLIt Tool is free to use

The Supervised Self-Organizing Map (supervised SOM, or SSOM) is based on clustering. These maps and their subsequent visualization help to monitor the progress of trends and magnitude of the degree of impairment. The algorithm of the SSOM represents a very effective classification approach, but it is only effective for well-known input data or for well-known classes of input data. ANNs were selected because of their notable robustness and strong ability to perform data visualization; hence, they can also process less qualita-

This study included 72 controls and 14 cases. The goal was to categorize the subjects into two classes, controls vs. cases. We obtained the results from the speech analysis via vowel map-

SSOM classification: SSOM was formed by a two-dimensional map with 24 × 24 units. The type of map had a hexagonal grid with a random initialization of the vectors. The following

(a) The first stage (rough): The Batch Map algorithm was used with the Gaussian neighborhood function, which decreased monotonically from 24 to 1. The training steps were set to 5000.

(b) The second stage (fine): The Batch Map algorithm was used with the Gaussian neighborhood function, which decreased monotonically from 2 to 0. The training steps were set to 1000.

(b) The child's speech can be recorded for later replay and evaluation.

en: *"nose"*) to "*los*" (based on real example).

20 Learning Disabilities - An International Perspective

therapist for a more detailed classification.

**8. Artificial neural networks analysis**

and is available from iTunes.

tive signals.

**8.1. Description of method**

ping with speech from cases by SSOM.

two stages of training were used:

The training data were set to the dimension of 31,475 × N, where N represents several speech coefficients. The number of wav-files was 1495, and the number of phonemes was 2299. **Figure 10** shows the classification via SSOM trained for vowels for cases. The left panel or part A of the chart represents a 2-D map, and the right panel or part B of the chart represents a U-matrix. These colors or parts of the map represent the vowels in the map; a red color (or the upper left part) represents *"a"*, an orange color (or the lower left part) represents *"e"*, a blue color (or the lower right part) represents *"i"*, a green color (or the upper right part) represents "o" and a yellow color (or the middle part) represents "u".

For the training set, the utterances of all controls and cases were classified with these maps. A white color indicates a successful classification, while a black color indicates a failed classification. For cases, there are characteristic replacements for these vowels, that is, *"o"* behind *"e"* and *"u"* behind *"i"*. These replacements are specific for cases and is not observed in controls. This method obtained a success rate for detecting children with SLI of more than 85%.
