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How To Meet The Demand For Software Engineers

How To Meet The Demand For Software Engineers, Companies everywhere are finding themselves in desperate need of competent, qualified software engineers

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How To Meet The Demand For Software Engineers

Companies everywhere are finding themselves in desperate need of competent, qualified software engineers who can help them remain relevant and efficient for years to come. As many leaders of these companies are discovering, however, attracting excellent software engineers to your team is easier said than done. Indeed, there’s a massive shortage of software engineers across a wide number of industries right now.

Here’s an analysis of the ongoing shortage of competent IT experts, and how companies, universities, and other actors can come together to meet the demand for software engineers.

1. The shortage is caused by growth.

If there’s an easy way to describe why businesses, universities, and other institutions are suffering from a shortage of IT workers right now, it’s that recent explosive growth has made it almost impossible to find enough talented workers. Companies everywhere are digitizing in general and embracing software in particular, a trend that requires savvy software engineers who can help these businesses develop or acquire good software in an affordable manner. What businesses everywhere have discovered, however, is that these software engineers are in high demand and thus gravitate to those positions with the best salaries and most competitive benefits packages.

The software engineering problem is also much more complicated than a simple lack of professionals. It’s also worsened by a lack of qualified professionals, as many individuals lack the certification or experience they need to become viable candidates for many of the open positions available right now. 2018’s shortage of software engineers illustrates that this shortage is about quality as well as quantity.

Once business owners understand this, they’ll realize that they need to offer competitive benefits packages and high salaries to good software engineers if they want them to join their teams and stick around for a while. Universities, too, are finding that software engineers don’t want to stick around for long unless they’re well-compensated. There are a number of ways to lure in software engineers, beginning with improving your brand image and ending with making non-traditional hires that could pay off in the long-term.

Related to that last point about non-traditional hires is the shortage of diversity when it comes to today’s software engineer cohorts. Discrimination and bigotry in the world of tech and the broader commercial marketplace have led to a shortage of qualified software engineers by turning some talented youngsters away from the field, for instance.

2. We need more diverse engineers.

The best way to meet the demand for software engineers, which is sure to keep on growing as time goes on, is to embrace diversity in the field of software engineering. This means that women, people of color, low-income individuals, and others who have been historically denied not only software engineering positions but also training and educational opportunities must be welcomed into the fold. More than a mere buzzword, diversity delivers results when it becomes an integral facet of a company or university’s policy.

This means that those individuals interested in starting an enterprise software development company should be aware that a diverse workforce is going to be needed if you want to be capable of meeting the staggeringly high demand for software engineers. Female developers have already done plenty of work teaching the rest of us about how to hire more women in the field, and that begins with fostering a more tolerant and welcoming atmosphere that anybody can feel comfortable in.

Beginning with job descriptions and titles and moving on to workplace toxicity before finally arriving at fair compensation, there are many ways that today’s software engineering firms and commercial enterprises could be doing more to welcome women into the field. Young students, in general, should be more familiar with computer science before they graduate and enter the workforce. Those from impoverished backgrounds who lack the technical resources to become software engineers must be offered means by which to educate themselves and become the engineers of tomorrow.

Meeting the intense demand for software engineers won’t be easy, but companies, colleges, and social movements across the country can surely come together to ensure that it happens. By beginning with better compensation packages that entice more people to the field and focusing extensively on diversifying future generations of software engineers, we can take positive steps right now that will ensure software engineering in the future is more efficient than ever before.

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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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