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Principles of Good Software Engineering

All of these factors have combined to make the software development scene far more competitive, which makes following the principles of sound software engineering all the more critical.

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As in project management, there are always competing priorities in software development. And a team inevitably finds itself having to make compromises to find the delicate balance between these priorities and constraints. And rarely, if ever, do they succeed in reconciling them successfully and adequately.

However, in today’s modern software world, this notion of compromise is no longer acceptable. Even “better, cheaper, and faster” is no longer good enough. Today’s customers want top quality, regular updates, and a competitive price, and sometimes, their demands go much further. Successful software development now requires adding far more value. Software vendors’ products and services must meet an ever-increasing set of criteria with no room for compromise.

As technological infrastructure costs have fallen, higher performance storage, memory, and processors are now available to all developers at a more affordable price. At the same time, software development tools and methods have undergone exciting changes, with open source and low-cost tools being far more widely available.

Now, a single engineer can deploy a web application that supports auto-scaling, consisting of a message queue and a database, all in just minutes, matching the same results that used to take the entire team days or weeks. By using a highly scalable and available cloud infrastructure, the cost of hosting an application is a fraction of what it was just a decade ago.

All of these factors have combined to make the software development scene far more competitive, which makes following the principles of sound software engineering all the more critical. In this article, we’ll lay some of the essential ones down.

1. Timeliness

You must deliver value to customers faster than your competitors while anticipating their growing expectations and ever-changing demands.

2. Adaptability

Vendors need to produce software that can adapt to ever-changing market conditions. Businesses need their software to adapt and deal with various abnormal market conditions in a world of supply chain constraints, tariffs, and unstable energy prices. They are long gone the days of rigid software design following pre-set formulae.

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3. Maintainability

If you want to earn long-term clients, you need to pay attention to maintainability. Technological advances have yet to overcome an inconvenient truth: 75% of software costs are related to maintenance and administration issues. Software design must contribute to the simplification and optimization of maintenance processes.

4. Automation

When it comes to development, it is essential to leverage automation tools and methods for the design and deployment phases and to limit the slowdowns caused when processes rely on manual tasks. As the demand for skilled developers grows, the cost of developers’ time increases. You don’t want your developers to spend hours doing unproductive tasks. Automation is an essential tool to help make your team more competitive.

5. Scalability

Scalability must be at the heart of the software architecture. To truly reach scalability, the software you design must be genuinely open to allow for growth from 20 to 20,000 and from 20,000 to 20 million users without having to touch the underlying software architecture or, worse yet, force the customer into a situation where the product must be replaced entirely.

But scalability isn’t just about the number of users: it’s also about the volume of data, the complexity of the application, and the number of developers that can concurrently work on the project.

6. Measurability

Software should measure and provide key metrics, reports, and business outcomes that DevOps, site reliability engineers (SREs), and business teams can leverage to improve business performance and success.

7. Resilience

In the event of a problem, a well-engineered software program must be able to recover its operation, quickly restore lost functionality, or prevent data loss to ensure that it does not negatively impact the business and customer experience. Regular backups, trace logs, and error handling play vital roles here.

Conclusion

In conclusion, it is not a matter of meeting some of the criteria here. If you want to be successful, It is imperative to meet all of them. Otherwise, a competition that provides excellent product engineering services will take care of it.

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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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