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Willpower and Procrastination: How to Stop Wasting Time

Is procrastination keeping you from success in your business? Procrastinating is one of the biggest drains on our energy because even when we don’t accomplish anything

Published

7 months ago

March 8, 2022

Marie Miguel

Willpower and Procrastination How to Stop Wasting Time

Is procrastination keeping you from success in your business? Procrastinating is one of the biggest drains on our energy because, even when we don’t accomplish anything, we still feel depleted from the effort of trying to stop procrastinating. Sometimes, no matter how much we want to do something, it feels like we can’t make ourselves do it.

Understanding how willpower works can help you understand why you procrastinate and how to stop. The key to making a change isn’t always working more or trying harder. Sometimes shifting your strategies and mindset is all you need. BetterHelp and other mental health resources can help you understand what’s going on in your brain and how to address it.

Table of Contents

1. Why We Procrastinate

Procrastination can seem like a mysterious process. Why would we want to waste time when we want to get something done? Procrastinating is often the brain’s response to negative feelings around the task you need to do.

Procrastination can come from:

Fear. Many people procrastinate because of the fearsome aspect of the task they have to do. You might be afraid of disappointing your boss or even of failing at a challenging task.
Self-Sabotage. Procrastination can also be a method of self-sabotage. In this case, you might procrastinate because you feel like you’re not worthy of achieving your goal or not good enough to do it. Your brain will then sabotage your work as a way of avoiding the adverse outcomes it is predicting.
You are feeling out of control. Our brains may also procrastinate in a misguided attempt to get control of our time. You might procrastinate because you feel like you have no free time or control over your schedule. Of course, this then backfires, leaving you even less time to do what you want.

2. How to Stop Procrastinating

a. Make a Plan

We often procrastinate because a task feels too overwhelming to tackle. If this is the case, try starting by making a plan. Break larger tasks into small, achievable steps. Your first step can even be something as easy as “open the document” or “read this article.”

Once you break up your task into steps, you can plan to finish it. How long will each step take? When can you work on it? Making a plan can help you feel more prepared and ready to do the work.

After you’ve broken down your task and made a plan, it will be much easier to start. You don’t have to think about how you will finish everything. Instead, you can focus on your next step. Once you’ve taken the first step to do a project, it will be much easier to follow through and finish.

b. Focus for Short periods

One mistake that many people make is trying to focus for hours. This may lead you to procrastinate as you dread the work hours ahead of you. You may even lose focus and start to procrastinate while you’re trying to work.

Instead, try focusing for short periods. One common way to do this is the Pomodoro Method. In this technique, you set a timer for 25 minutes and concentrate intensely during that time. Then, you can take a 5-minute break.

Breaking your work into smaller chunks of time will make it less intimidating. Once you get yourself to start working for 25 minutes, you may even want to keep working once the timer goes off.

c. Take Breaks

It’s essential to take breaks throughout your workday. These can help you take a step back and look at your work with a fresh perspective, making it easier to solve any issues and come up with new ideas.

Planning breaks throughout your day will also give you something to look forward to. You may be more motivated to work if you know that you can eat your favorite snack or watch a bit of your favorite show once you finish.

d. Be Realistic

Procrastination can also be a symptom of burnout. If you constantly feel stressed and overworked, your brain will rebel by trying to steal little moments when it can.

To avoid procrastination, it’s essential to be realistic about your schedule. Make sure the things on your to-do list are things you can accomplish in the time you have. This will allow you to focus more easily and feel that sense of accomplishment when you check everything off.

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.

Published

2 days ago

September 28, 2022

TwinzTech

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.

Table of Contents

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.