However, for speech technology to reach the potential of what it, uniquely, can do well, it still has much further to go

As many of you seek to find a career that suits your interests, or for those who are looking to make a career switch, you might have pondered on the thought of becoming a data scientist. And since you’ve probably only heard about the good parts of data science, I’m going to give you 4 reasons why a data science job might not be for you.

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ws a bit of everything; every step of the project, because of that, they can offer better insights on the best solutions for a specific project and uncover patterns and trends. Moreover, they will be in charge of researching and developing new algorithms and approaches.

Coding-problems contains an updated collection of solutions to numerous coding and algorithmic problems that new programmers face while learning data structures and algorithms. Meto has covered the solutions for problems from various topics and has categorized them into arrays, linked lists, dynamic programming, math, and a few more categories.

Lastly, like everything in tech, data science is constantly evolving. For example, it was only in 2015 that TensorFlow was created, and now, it’s one of the most in-demand skills. As new technologies are created and iterated to be better, you’ll be expected to learn new technologies as well.

In 2016, Google came up with a new approach to speech synthesis, using WaveNet, a neural network, which can be trained to generate almost any kind of sound, and then training it with real human speech. Once trained, it can be fed with quite robotic synthesized speech, and then make it sound human.

Data science is an extremely broad term, which means that it means different things for different companies, and so, different companies desire different skillsets. For example, some companies desire machine learning knowledge others desire experimental design and A/B testing, some desire Python programmers and others desire R programmers, etc… Therefore, the skillset you develop at one company won’t necessarily carry you through your entire career.

A lot of your time will most likely be spent on understanding the data, where it’s coming from and how it’s being manipulated), as well as preparing the data (EDA, data wrangling, feature engineering, etc.). And if you don’t have the data that you need to develop a good model, you’ll have to develop the pipelines to be able to get the data that you need.

A lot of people seem to have this false notion that a data scientist’s job consists only of building these revolutionary, intricate, and impactful machine learning models day and night, and this is simply not true.

I’m sure most of you have seen HBR’s article, “Data Scientist: The Sexiest Job of the 21st Century,” and they’re not the only ones to have claimed that data science is the holy grail of all jobs.

On top of that, data science is a multidisciplinary job. You’re expected to have a certain level of knowledge in programming, statistics, mathematics, business understanding, etc… which means there you’ll ALWAYS have room to grow in one of these areas.

Technology to separate speech from different speakers is coming on in leaps and bounds. This is achieved both by analysing the speech more deeply, and by combining the audio data with other sources, like using multiple microphones to measure relative volume and direction, or by using input from cameras to add lip movements and facial expressions to the mix.

Nowadays, the latest developments are routinely shared, and the whole industry takes the latest ideas from Google, NVIDIA, Microsoft and a global community of university researchers, and with their blessing, extends them and applies them in new contexts, adding expertise from their own niche professions.

I have spent a lot of time working on systems which analyse accents, mispronunciations and speech impediments. Some people are difficult to understand because they have an unfamiliar accent, or are only just learning a language. We can make it easier to master pronunciation by giving them real-time feedback, but maybe we needn’t bother: morphing accents, and correcting errors in real time are both becoming a reality.

Technology has to get as good at listening, and at speaking, as human beings are, and then — in some contexts — get better than we are. Here are a few examples from projects, which I and others have been working on lately.

Away from our headsets, speech isn’t really as linear as I have made out. In close proximity to someone speaking, I might whisper a comment to another listener, and still go unheard by anyone else. At a dinner party, I might be involved in more than one conversation at a time, because it is easy, in the 3D space of the real-world, to keep track of who has said what, and to control the volume and direction of my speech to target a specific listener.