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Movies from 2018 to Demand from Your Cable Provider

It was a good year for the superhero genre, but there were many unexpected flops when production costs were accounted for.

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2018 has already been a year of some of the biggest box office hits, and so do flops. Some high gross-ers like Black Panther, the third highest grosser in the US film history, Deadpool 2, Avengers-Infinity War, and Incredibles 2. It was a good year for the superhero genre, but there were many unexpected flops when production costs were accounted for.

Just pick up the phone, dial wave phone number, request any title of your choice, and enjoy the limitless options that wave on-demand has to offer. Read on, watch them, and see if the following ‘flops’ were justified or were they just a marketing failure.

1. A Wrinkle in Time

It won’t be wrong to say that a wrinkle in time was one of the much-anticipated novel-turned-film, with a star cast having Oprah Winfrey, Reese Witherspoon, Mindy Kaling, Storm Reid, Chris Pine, and Zach Galifianakis. With an approximate production spend of 100+ million dollars, the Disney movie collected only $100 million domestically and a mere $32 million internationally.

However, when Marketing and promotion costs were accounted for, the film was categorized as a box office bust. Since the break-even target was over $200 million and $132 million is very disappointing compared to that. This has been Disney’s worst bomb so far, and hence expectations of a sequel are low.

2. Solo: A Star Wars Story

What else could be a more awaited film than the one from the Star Wars franchise? It won’t be termed a flop in a layman’s sense since it is the 6th highest-grossing to date this year. With a budget crossing $250 million, it managed to make $344 million, whereas the domestic collections were around $197 million.

They are heading in the direction of a good break-even, but a Star Wars movie is made to break records and not just break even. Considering these standards, this Star Wars movie became the lowest-grossing movie in the franchise to date. With so many films lined up before and after Solo’s release, it was under solid competition in attracting the audience mainly from its studio.

3. Tomb Raider

As surprising as an entry in this film in the said list, we have our reasons to put it here. Tomb Raider starring Alicia Vikander was domestically a flop among the U.S. audience. It managed to bag only $57 million in the domestic market and hence considered a defeat considering a production spend of $94 million. The studio, fortunately, managed to make money from the international market with an additional hefty sum of $216 million as global sales.

4. Gotti

With a strong cast, starring John Travolta Gotti is a criminal biopic yet a big flop this year. A biographical drama depicting the life of New York City’s gangster John Gotti. The biopic ended up with a zero percent rating from Rotten Tomatoes, which was later criticized by the film’s marketing team. The movie had a production expense of around $10 million. On its opening, it made only $1.9 million far from the production budget, eventually earned $4 million in domestic sales, and hence categorized as a big flop.

5. Death Wish

Bruce Willis has seen his golden years in the ’80s, and 90’s such that every movie he did was a hit, but his Midas touch didn’t work here when it came to Death Wish. With a budget of $30 million, it managed to make hardly $48 million in sales along with not-so-good critic reviews. The film’s release date got delayed multiple times due to U.S. school shooting incidents; hence it couldn’t find the right time of release in 2018. This was one of the reasons this didn’t go well for Bruce this time.

6. Sherlock Gnomes

After a massive success of “Gnomeo and Juliet” with collections of $194 million against a production budget of a mere (as compared to the sales) $36 million, the MGM production house decided to go with another animated adventure movie, with a budget of $59 million.

Unfortunately, it turned out to be a sequel that wasn’t needed. Although starring the voices of people like James McAvoy, Johnny Depp, and Emily Blunt, the film managed to make $43 million domestically and hence was a domestic flop, as it never reached the break-even point. Since the movie did better in the international market, doing a business of nearly $87 million.

7. Action Point

A bad amusement park comedy starring Johnny Knoxville, Action Point, released this year, costing the producers more than $19 million. And suppose the marketing costs are accounted for. In that case, the Paramount Studios might end up losing somewhere around $30 million on this film, which they invested after getting 4 Jackass blockbusters by Johnny Knoxville earlier. With a meager rating by the critics, the film managed to do a business of $5 million only and, hence, is termed a big flop!

Now off you go, woke-folks, and discover for yourself which of these films deserved a second chance, and their doom was not justified when considering the quality.

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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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