Guide to Speech Synthesis through Machine Learning


speech Synthesis

Each and every day, we create 2.5 quintillion bytes of data and the pace is constantly increasing. During the last two years, we have created 90% of the data that has ever been created and many data is through speech synthesis. It’s a world full of data. As such, humanity needed to create systems in order to organize all this amount of information.

These systems evolved to increasingly sophisticated stages and finally they have become indispensable. Let’s follow the timeline of their evolution from machine learning towards speech synthesis:

Machine learning — A new era has begun

The video suggestions you receive on Youtube, the matching images you see on Pinterest, the results you get on Google when you type in a keyword, your social media feed or Siri, your voice assistant — all these are day-to-day applications of machine learning. Could we live without machine learning? Yes, but we would give up the great amount of comfort that Artificial Intelligence provides for us. And why would we?

It’s basically a simple process: platforms such as Facebook or Youtube collect data about us and our preferences and, with the use of machine learning systems, they predict pretty accurately what you want to receive next, so they can deliver that exact thing.

In a similar manner, your microphone records your voice command, sends it to the appropriate service and then you receive a relevant response back to your device from voice assistants like Alexa or Siri. Through machine learning, the voice assistants learn how to respond in a satisfactory manner rather than being taught how to use the data they have access to.

The long and the short of it, machine learning uncovers the pattern in our actions and preferences and applies this pattern to meet our next request or need.

The rapid evolution of machine learning towards speech synthesis

Obviously, machine learning hasn’t always been this advanced. Before the 1950s, we only discovered and refined the statistical methods. After the 1950s, a series of simple algorithms were developed in order to conduct the first machine learning research.

In the 1960s, machine learning gained a bit of momentum with the use of Bayesian methods and probabilistic inference. However, the effectiveness of machine learning came into question in the 1970s and that was the period of what we now call: “The Artificial Intelligence Winter”.

The new studies on backpropagation conducted in the 1980s fortunately led to a resurgence in the research methods of machine learning. By the 1990s, the approach became mainly data-driven, as opposed to the knowledge-driven approach used in the later decades. Programs that could analyze large amounts of data and learn from the results were created. Support vector machines (SVMs) and recurrent neural networks (RNNs) gained popularity.

Today, we watch in awe how accurate deep learning functions have become and how the greatest part of our lives is influenced by software and applications which use speech synthesis, Support Vector Clustering, Kernel methods and both supervised and unsupervised machine learning methods.

Why all this fuss about machine learning?

Ever since the microchip, Machine Learning may as well be the greatest technological innovation. If we learn how to use its power, we will evolve into a new technological era.

Self-driving cars? Digital personal assistants? Smart homes? Easier commuting through traffic predictions? Email filtering? Search engine result refining? Online fraud detection? You’ve got it!

The most important aspect of machine learning is its ability to improve our businesses, our schedule, our health and the quality of our life. If we enable our machines to analyze, test and ultimately to learn, they will have the capacity to teach us how to live better and happier lives.

One of the main applications of machine learning in today’s technology is in the area of artificial production of human speech, namely in speech synthesis.

Speech synthesis through machine learning

With the help of a speech synthesizer, any text can be converted into speech. This system is called text-to-speech. It has two parts: the front-end which phonetically transcripts each word and divides the text into phrases, clauses and sentences, and the back-end (the synthesizer) which converts the symbolic linguistic representation into sound.

The better the speech synthesizer is, the greater its similarity to the human voice is. Even better yet, we have the speech-to-speech synthesis. It is enough to have a sample of someone’s voice and the system can accurately recreate it whenever we need. Out of a simple sample of that voice, the system can create any discourse. The human ear cannot differentiate between the original voice and the synthesized one.

2020 Guide to speech synthesis through machine learning

Most probably, you’ve already experienced speech synthesis more than once: Siri, the famous virtual personal assistant, Google Home and all kinds of chatbots. How do they get to speak with us in such a humanly manner? Let’s analyze the process:

1) The concatenative approach

This is a widely used technique for speech synthesis. Firstly, we need to have a pretty large database with pre-recorded sequences of speech and secondly, we concatenate them into a completely new audible speech. The limits of this approach are the difficulty in scaling (a new data set is required every time we need a different style of speech) and the robotic sound we get (the final synthesized product lacks the consistency of natural human speech).

2) The parametric approach

The difficulty in scaling of the above approach led to the use of parametric models. Through these models, we can control the generation of speech by way of the input definition.

The parametric models focus on acoustic feature generation and, with the help of the vocoders, converts the input into acoustic. Basically, a recorded human voice is modified with a set of parameters in order to change it according to our needs.

However, these two approaches are progressively replaced by the modern approach in speech synthesis called deep learning.

3) The deep learning approach

Speech synthesis through machine learning arrived at the point of real-time voice cloning. With just a short sample of someone’s voice, any new dynamic and unique voice content can be created. The system immediately learns the voice, its intonations, the pauses in between words and sentences, the emphasis, volume and rate and delivers text-to-speech utterances with the exact same characteristics of the original sampled voice.

Ethical? Sure, if the owner of the sampled voice offers his consent!

How do we use machine learning for voice cloning at Respeecher?

First of all, we use it ethically. We don’t process any voice sample if we don’t have the written consent of the owner and we do not allow any deceptive uses of our AI technology.

Other than that, it is a quite simple process: we collect the target and the source voices, do our Artificial Intelligence magic and voilà! You have the voice that you need at your disposal.

You don’t have to worry if the actor is no longer available, because, with a sample of his voice, you can have any discourse you want in that actor’s voice. Also, our voice cloning service makes actors’ work way simpler, as they don’t have to spend long hours in the recording studio anymore. In this way, we help the companies that produce video and audio content (movies, animations, video games, advertisements) to become more time and cost-efficient.

The tedious and costly dubbing process is now as simple as saying “voice cloning”. We can generate discourses in any voice we have a sample of, so that we can bring back the voice of an actor who passed away, for example.

Respeecher is the simplest and most professional way to create endless amounts of audio for any kind of project: film and TV, gaming, advertising, animations, podcasts and audiobooks, healthcare, call centers. Leave the voice logistics of your business in our hands and we will help you replicate the perfect voice for your project. We aim towards an even wider reach of our voice cloning services in the future.

This isn’t text-to-speech, but speech-to-speech technology, and that’s why our cloned voices will never have that robotic, non-emotional sound. We deliver excellent results: the human ear is not able to distinguish the real voice from the cloned one.

Intrigued by speech synthesis through machine learning? You can watch some demos on our Youtube channel. Beware, you might be in awe after you see what we can do! Also, please subscribe to our newsletter and we promise to keep you posted with the newest and most enticing news about speech synthesis through machine learning!