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How to Prepare for an Employment Background Check

The last component of employment background checks is credit reports, which contain information about an applicant’s financial operations.

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Experience and education are the two things job candidates will misrepresent their resumé most often. Even influential people are not immune to the temptation to lie about them. Prominent figures like Scott Thompson, former CEO of Yahoo, embellished some college degree data, leading to his premature termination.

According to UnMask.com, employment background checks have the following main components: education verification, reference checks, employment history verification, and professional license verification.

Reputable employers will carry all of these out, so it’s not a good idea to lie on your resume. You can’t blame any company – any new hire is an investment they want returns on. According to recent reports, 33 per cent of resumes contained erroneous job descriptions, and 29 per cent showed inaccurate employment dates.

1. Things to Know About Criminal Activity Checks

Criminal record checks are the first thing that comes to mind when you think of background screening. They are the most significant and most challenging type of inspection a prospective employer can run on a job candidate. A person’s criminal history affects job qualifications, job performance, and a company’s security and safety.

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2. Types of Records

There are three types of criminal records: county, state, and national. A lot of companies rely on national databases only. These consist of several state and county criminal record repositories and are usually maintained by data brokers. Wise employers do not rely on federal checks alone because they tend to be incomplete. The FBI found half of all criminal records were incomplete in a random audit.

To create a state database of criminal records, local jurisdictions are obligated to report individuals’ criminal records to the state. This might lead you to believe there is a stable and reliable source of state criminal records.

After all, it’s only logical to draw such a conclusion. In reality, however, authorities cannot execute this process properly because of staffing, administrative, and technical issues, leaving many gaps in state records.

3. Downsides

Employers who only use state data can miss unreported convictions, dismissed cases, and pending cases. This is because records can take years to reach state databases of criminal records.

County checks are the most challenging and time-consuming but also the most reliable check form. County databases are updated and highly accurate. Attentive employers will run checks for each county where a job candidate has lived. Statewide and national checks are an excellent way to supplement county records because they will reflect offences committed in different states.

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4. Checking Driving History

Any company will want to hire a responsible driver, especially if driving is part of the job. Traffic accidents or tickets are a red flag if a candidate is being interviewed for a delivery job, for example. Even if the position doesn’t require MV operation, an employer will want to make sure their staff is safe on the road.

Motor Vehicle Records (MVRs) are a great source of information to check someone’s driving record. They also help employers confirm names, addresses, dates, birthplaces, and other necessary identity information.

While DUI and reckless driving are felonies, they do not surface in criminal records in many states, but you can see them in MVRs.

5. The Last Component: Credit Reports

The last component of employment background checks is credit reports, which contain information about an applicant’s financial operations. This report will show if and what loans someone has taken out, if they pay their bills regularly, or if they’ve ever filed for bankruptcy.

According to the EEOC, credit reports should not be used for employment-related purposes. Men and Hispanic and African-American applicants are more likely to have low reports, risking unlawful discrimination in the hiring process. Industry experts and employers disagree with the watchdog. They find credit report data can be of paramount importance in certain situations.

Either way, employers should make sure credit checks are needed before ordering them. These are only recommended for financially responsible upper-level positions, such as senior management. They are also advisable if the person will have direct access to non-monetary assets or cash.

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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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