Secret of Languages

This repo hosts a visual analysis of 17 different spoken languages in the world.

For more details, please refer to the Kaggle Kernel.

In this analysis, I will visually demonstrate how these languages are different in certain attributes.

Datasets

There are two data sets used in this study the Languages and the Spoken texts data sets (found here and here).

Below is a sneak peek at both data sets following with an overview of features in each.

Languages dataset

Column	Description
iso_lang	ISO_639-3 language code
language	Language name
information density	Bits of information per syllable in the language
distinct_syllables	The number of different syllables in the language
continent	The continent where the language is spoken

Spoken texts dataset

Column	Description
speaker	Speaker ID
iso_lang	ISO_639-3 language code
text	Text ID
sex	The sex of the speaker
duration	The number of seconds it took to speak the text
syllables	Number of syllables uttered during the speech
age	The age of the speaker

The following plot shows how the trend that explains the relation between information_density and distinct_syllables divides the whole bunch into two clusters. It is worth-mentioning that French and Mandarin are outliers in each cluster.

Once speech_rate defined as distinct_syllables/duration is plotted against information_density, a subtle point is found.

Technically, a high value in both features (information_density & speech_rate) would indicate a high information rate and efficient communication (a higher number of information bits conveyed per second).

It looks like we do not have a language that is high both in information_density and speech_rate, which could indicate that human minds are not good at processing auditory information beyond a certain rate limit.

Let's take another step deeper and define information_rate as information_density ✕ speech_rate. This new feature holds the amount of information per second conveyed by each speaker.

Now let's plot information_rate for each language in the study to see if there are at least small differences between them.

Conclusions

• In an aim to investigate the potential differences and similarities of languages, 17 different languages from two continents (Asia and Europe) were studied. In the first step, I decided to evaluate the relatioshship between the information_density (bits of information per syllable) and the number of distinct syllabus for each language. Results proved that firstly as expeted the higher the number of distinct syllabus, the greater the information_density is. Secondly, the data for Asian languages cluster close to each other. Same story goes with European languages. Of course some outliers like French and Mandarin were also observed.

• In the second step, I studied the relationship between information_density (bits of information per syllable) and speech_rate (syllables per second, which is technically equal to number of syllables per duration) for the languages. Given the fact that a high value in both information_density and speech_rate would indicate a high information rate and efficient communication (a higher number of information bits conveyed per second), the reciprocal nature of these two variables confirmed the fact that we do not have a language that is high both in information_density and speech_rate, proving that human minds are not good at processing auditory information beyond a certain rate limit.

• In the final step, I decided to plot the rate at which information is conveyed (information_rate) for all the languages to see if there are at least small differences between them. Results show that the difference between the languages are not that significant. Thai is standing at one extreme with the lowest information_rate whereas French is on the other extreme with the highest information_rate among all languages. Leaving us with the very important conclusion that if you don't know French yet, you 'd better find a good teacher as soon as possible.