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Top 5 Countries That Still Have Limited or No Internet Access Even Today

Top 5 countries that still have limited or no internet access even today. If you decide to visit this country, it is best to take care of internet access in advance for the entire trip.

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Many words have already been saying about in which countries the Internet is developed better. In these predominantly European states, almost every citizen is an active user of the Internet. Advanced technologies allow you to connect to the Internet almost anywhere quickly.

Countries like Denmark, Norway, and Sweden are at the top of the Internet rankings. But there is another side to the rating. Let’s consider which countries you should not go to in search of free Wi-Fi and fast connections.

1. East Timor

Officially the poorest country in Asia. The small state occupying the eastern part of a little island north of Australia has a sad history. Most of the time, the entire island of Timor was a Portuguese colony. In 1975, East Timor declared independence, only to be occupied by Indonesia nine days later.

Only twenty-seven years later, in 2002, the country declared independence again. Nevertheless, social unrest periodically occurs there, and high seismic activity and tsunami exposure also do not contribute to technology development. In East Timor, only 0.9% of citizens have access to the Internet.

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2. Myanmar (Republic of the Union of Myanmar, ex Burma)

The state is located south of China and, probably, has adopted the manner of interfering in the affairs of the Network from its “big brother.”

The development of the Internet in Myanmar is hindered by stringent online censorship. It gets to the point of absurdity – the authorities have the right to read users’ emails and officially record all bloggers. In 2008, one of the bloggers was sentenced to 59 years in prison for posting a video of the destruction caused by the cyclone on the Internet.

True, he was released three years later. As a result of this policy, Internet access is widespread mainly in Internet cafes. In this country, only one person in a hundred goes to the global web.

3. Burundi

The country, located on Lake Tanganyika’s shores, is officially recognized as the lowest state in the world. From 1993 to 2005, a civil war raged in the country, as a result of which about 300 thousand people died. For comparison, the capital of Burundi has less than 400,000 inhabitants. Given such a sad picture, one should not be surprised that the country’s Internet is hugely underdeveloped. It is used by only 1.1% of the population.

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4. The Democratic Republic of the Congo (ex Zaire)

One of the largest African countries, located in the heart of the continent. The size and relatively rich natural resources, in this case, had almost no effect on the welfare and development of the country.

To this day, corruption in the Congo reaches colossal proportions because during the dictator Mobutu Sese Seko was almost a state institution. From 1994 to 1999, there was a civil war in the country, the echoes of which can be heard to this day.

Technology development is relatively weak. The Internet can be found mainly in hotels, Catholic missions, and rare Internet cafes in the largest cities. 1.2% of the population uses the Internet more or less actively.

5. Ethiopia

Internet policy in this East African country is a prime example of conflicting government action. On the one hand, the government considers it necessary to develop technologies to raise the population’s level of education. On the other hand, it censors the Web and monitors a few users.

Ethiopia has one of the lowest numbers of personal computers in the world. The Internet can be found in major cities, and Wi-Fi is available in various places in the capital. Only 1.1% of the population uses the network.

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Of the entire list, Ethiopia is the most popular among tourists, who have a unique national cuisine. If you decide to visit this country, it is best to take care of Internet access in advance for the entire trip. To avoid regional blockages, it is best to use virtual private networks (VPNs). A reliable VPN protects your data from the eyes of Ethiopian IA.

Many articles & posts of travel bloggers are devoted to choosing a useful VPN for traveling in Ethiopia. So, when examining the Cyberghost VPN vs. NordVPN, it was revealed that both of these applications are doing quite well with their duties in almost all countries of East Africa.

Other leaders on this sad list include countries such as Somalia (1.3% of users), Niger (1.3%), Guinea (1.3%), Madagascar (1.9%), and Chad (1.9%). We hope we haven’t disrupted your travel plans with this rating!

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Internet

How do collect and train data for speech projects?

Data collection is the process of gathering, analyzing, and, measuring accurate data from diverse systems to use for business process decision-making, speech projects, and research.

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How do collect and train data for speech projects

With technology evolution, we are moving towards machine learning systems that can understand what we say. In our daily lives, we all have encountered many virtual assistants like Alexa, Siri, and others. These virtual assistants often help us in tuning the lights of our homes, finding information on the internet, and even starting a video conference. But do you know how it does that?

To produce results, these virtual assistants use natural language processing to understand the user’s intent. Natural Language Processing technology enables virtual assistants to understand user intent and produce outcomes. Basically, these virtual assistants are applications of automatic speech recognition and are also known as speech recognition software. This software uses machine learning and NLP to analyze and convert human speech data into text.

But, attaining maximum efficiency of these software requires the collection of substantial speech and audio datasets. The purpose of collecting these audio datasets is to have enough sample recordings that can be fed into automatic speech recognition (ASR) software.

Furthermore, these datasets can be used against the speakers using unspecified speech recognition models. And to make ASR software work as intended, speech data collection and audio datasets must be conducted for all target demographics, locations, languages, dialects, and accents.

Artificial Intelligence can be as intelligent as the data given to it. Hence, collecting data for feeding the machine learning model is a must to maximize the effect of ASR. Let’s discuss steps in speech data collection for effective automatic speech recognition training.

1. Create a Demographic Matrix

For creating a demographic matrix, the enterprise must consider the following information like language, locations, ages, genders, and accents. Along with these, it is a must to note down a variety of information related to environments like busy streets, waiting rooms, offices, and homes. Enterprises can also consider the devices people are using like mobile phones, headsets, and a desktop.

2. Collect and transcribe speech data

To train the speech recognition model, gather speech samples from real humans and take the help of a human transcriptionist to take notes of long and short utterances by following your key demographic matrix. In this way, human is a vital and essential part of building proper audio datasets and labeled speech and further development of applications.

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3. Build a separate test data

Once the text subscription is completed, it’s time to pair the transcribed test with the corresponding audio data and segment them to include one statement in each. Later on, take the segmented pairs and extract a random 20% of the data to form a set for testing.

4. Train the language model

To maximize the effectiveness of the speech recognition model, you can train the language model by adding general additional text that was not additionally recorded. For example in canceling a subscription, you recorded one statement that ‘I want to cancel my subscription, but you can also add texts like “Can I cancel my subscription” or “I want to unsubscribe”. To make it more effective and catchy you can also add expressions and relevant jargon.

5. Measure and Iterate

The last and important step is to evaluate the output of automatic speech recognition software to benchmark its performance. In the next step take the trained model and measure how well it predicts the test set. In case of any gaps and errors, engage your machine learning model in the loop to yield the desired output. 

Conclusion

From travel, transportation, media, and entertainment, the use of speech recognition software is evident. We all have been using voice assistants like Alexa and Siri to complete some of our routine tasks. To effectively use this speech recognition software requires proper training in the audio datasets and the use of relevant data for the machine learning model.

Proper execution and the right use of data make sure the speech recognition software going to work efficiently and enterprises can scale them for further upgrades and development. As data and speech recognition go hand in hand, make sure you are using data with the right approach.

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