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4 Barriers to Effective Time Management and How to Overcome Them

Time management skills are essential for succeeding in the workplace, at school, or in meeting our personal goals. For more articles and information about time management, visit BetterHelp.

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4 Barriers to Effective Time Management and How to Overcome Them

Time management skills are essential for succeeding in the workplace, at school, or in meeting our personal goals. However, so much can get in the way when it comes to using our time wisely to accomplish the tasks in front of us. This article will look at four barriers to effective task management and discuss strategies for overcoming these common hurdles.

For more articles and information about time management, visit BetterHelp.

4 Barriers to Effective Time Management and How to Overcome Them

1. Lack of motivation

Perhaps you have plenty of time to complete a task but can’t find the energy or motivation to get started. Multiple factors may contribute to common causes such as mental disorders, the desire to avoid unpleasant feelings accompanying a task, and lack of personal investment in a specific goal. It can help to identify what may be causing your lack of motivation.

To overcome this barrier, start by showing yourself self-compassion. Rather than start beating yourself up, turn towards yourself with gentleness and understanding. Studies have shown that self-compassion, not harsh self-criticism, actually increases motivation. Other strategies that may help combat low motivation include:

  • Acting motivated and letting your behavior inform your emotions.
  • Permitting yourself to quit a task after 10 minutes (as getting started is often the most challenging part).
  • Setting small goals while rewarding yourself along the way.

2. Procrastination

Another common barrier to task completion is procrastination, delaying a task despite the negative consequences. Researchers discovered that this behavior goes deeper than ineffective time management, as it is rooted in challenges with emotional regulation. People who procrastinate typically avoid complicated feelings associated with a task, such as anxiety, self-doubt, or boredom. Delaying the task at hand provides a sense of relief at the moment but only exacerbates feelings of self-shame, anxiety, and distress in the long run.

When you find yourself procrastinating, take a moment to check in with yourself and identify what feelings may be under the surface. Do you doubt your ability to carry out a task successfully? Are you placing unrealistic expectations on yourself to do it flawlessly? Or are you worried by the thought of criticism or judgment from others? Identifying what may be fueling procrastination can help you address what really may be going on under the surface and seek support if needed.

It can be beneficial to have someone hold you accountable for taking small steps towards your goal when it might feel tempting to avoid at all costs.

3. Challenges with prioritization

Having many different tasks to complete can make it difficult to know where to begin. Many individuals find it difficult to prioritize or determine which studies to focus on at any time and which can wait.

Start by making a to-do list, jotting down all of the tasks you have to complete on paper. Then, go through and separate the urgent tasks with strict deadlines from those that are important but not as time-sensitive. Order your studies, from the most critical to least critical, to get done on a given day.

Use this list to help you block out specific time in your day to tackle each task, starting with #1. If you don’t finish everything on your list that day, move those items onto the next day’s intentions.

4. Distraction

In today’s day and age, distractions are everywhere. Whether working from home or back in the office, you may find it tempting to start scrolling through social media or surfing the web.

To minimize distractions, it can be helpful to identify what has the potential to redirect your attention. If it’s noises in your surrounding area, you might put on earphones or find a different location to work. If it’s the chime every time, a new email comes in, set specific times to check your messages, and outside of that, close out of your account.

To avoid the cue to keep checking your phone, place it somewhere you won’t see or hear it. Taking regular breaks can also help to improve your focus on what you have to get done. After every half an hour or hour of work, be intentional to step outside for some fresh air or grab water or a snack. This can help you sustain focused attention when you are plugging away at the task.

Marie Miguel has been a writing and research expert for nearly a decade, covering a variety of health-related topics. Currently, she is contributing to the expansion and growth of a free online mental health resource with BetterHelp.com. With an interest and dedication to addressing stigmas associated with mental health, she continues to specifically target subjects related to anxiety and depression.

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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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