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7 Reasons to Switch from an Android to an iPhone

The software is better, the security is better, the device life cycles are longer, the phone cases are more excellent — look, this is just the introduction. Reasons to switch from an android to an iPhone.

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iOS is extremely user-friendly

Like any great rivalry, each side of the Android vs. iOS debate has its passionate defenders. So, if you’re considering switching from Android to iOS and you’re looking to hear a balanced perspective from both sides, we encourage you to do so — somewhere else. Because, friend, we’re not going to beat around the bush: We love the iPhone, and we think it’s better.

The software is better, the security is better, the device life cycles are longer, the phone cases are more excellent — look, this is just the introduction. We don’t have the space to get into it all here! Read on, and we’re going to tell you exactly why we think the iPhone ecosystem leaves Android in the dust.

1. iOS is highly user-friendly.

For most iPhone users, the smooth and responsive interface of iOS has long been the core attraction of the phone. If you’ve never tried it, we recommend you go to an electronics or mobile store and spend a few minutes scrolling through an iPhone. We bet you can find where everything is, even if you don’t know anything about iOS!

Moving to iOS means accepting the loss of some customization features, and we know that many hardcore techies will stick with Android until the end of time. However, if you want a user-friendly phone with good specs, Apple has spent the last 10-plus years perfecting precisely that.

2. Apple provides five years of software support (or more) for every iPhone.

Notwithstanding the controversy over throttling its phones (a feature that users can now turn off quickly), Apple’s phone longevity is among the longest of any manufacturer. The company is committed to offering a full five years of updates and support for every iPhone model, and that number is more like six or seven for many models.

Did you notice that the iPhone 6S, a phone that came out in September 2015, still got the latest iOS update? While that phone will probably have its ride into the sunset soon, it’s still crazy impressive that a phone so old still actively receives new software updates. Apple’s commitment to phone longevity sure does seem like the real deal, and it blows pretty much any Android phone out of the water in this category.

3. The Apple tech ecosystem is still second to none.

Don’t you love it when your gadgets all work together? Another significant advantage of being an iPhone user is that you automatically access iCloud, Photos, and many other apps from the Apple ecosystem. They’re all designed to integrate seamlessly, and by and large, they succeed in offering a low-friction user experience.

That doesn’t mean you’ll need to suddenly become the “all Apple, all the time” type. An iPhone will work just fine with your Windows PC or other non-Apple devices. But the more you invite the Apple ecosystem into your digital life, the more you’ll be able to use Apple’s powerful cloud-based features that give you access to all of your data all the time.

The Apple tech ecosystem is still second to none

Source: Hadrian/Shutterstock.com

4. Apple offers many iPhone models with great bang for the buck.

You might associate iPhones with the people lining up around the block at midnight to get the newest and top-of-the-line models. Here’s the thing, though: Many older and more basic iPhone models are still great phones, without the price tag of their more luxurious siblings.

Tip number one: Apple almost always cuts the price of the previous iPhone generation significantly when a new model launches. Many people choose to snap up the previous model, which should remain in fighting form for several years. (There’s that software longevity again!)

Apple also offers more budget-friendly models in their newer phones, including the iPhone 13 Mini and the iPhone SE. While these still cost more than Android budget models, you get what you pay for with a high-quality phone that you can keep around for a long time.

5. iPhones are more secure out of the box.

Yes, you can still make an Android phone reasonably secure with a bit of tweaking. But unless you love installing security apps on your phone, an iPhone is still the better choice because it’s far more secure by default. iPhones aren’t immune to viruses and malware, but they benefit from Apple’s tight control over its technology.

Android is open-source, which means anyone can examine its source code. Vulnerabilities in Android get found a lot more often and can be exploited much more widely. Android users can also download third-party apps that don’t go through an approved app store like Apple’s. These are selling points for some tech-loving users, but again — is the tradeoff worth it for the average person?

6. Apple’s Bionic chipsets are way ahead of the competition.

iPhones are generally faster and provide better app performance than their Android competitors. That’s because Apple now manufactures its silicon processor chips, and the Apple Bionic series has produced a giant leap in iPhone performance.

The first Apple chips arrived in the iPhone 12 and instantly stood out for their zippiness. The iPhone 13 has the successor chip, the A15 Bionic, which has received similarly glowing praise for its performance. The bottommost line is that if you want a fast phone, and especially if you do a lot of mobile gaming, you might never go back once you’ve gone Bionic.

7. iPhones have a fantastic range of fabulous accessories available.

If you have been an Android user for a while, you’ve probably noticed that most electronics stores have a far more extensive range of accessories for the iPhone than for Android models. From phone cases to chargers to accessories you never thought of like AirPods case covers, you’ve got tons of options for accessorizing any iPhone.

iPhones have an amazing range of cool accessories available

Source: Yalcin Sonat/Shutterstock.com

It’s OK — you don’t have to admit how painful it was that they never put your favorite minor league baseball team logo on a case for the Motorola Razr revival. iPhone 13 cases are available in a breathtaking variety, and we’re pretty sure you’ll be able to find anything you need.

Finally, yes, wired headphone users may mourn the loss of their 3.5 mm headphone jack. But most Android phone manufacturers are getting rid of theirs anyway, so come on over to the wireless side — it’s pretty nice! If you’re still not on the Bluetooth wave, remember that there are great earbuds available with a built-in Lightning connector, or you can always use a dongle.

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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.

6 Reasons to Transcribe Audio to Text

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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