How does speech recognition technology work

Once, the smart device has understood your tone, diction, and speech, it will then decode it into its own language and digitize it on your designated format. This is the finished speech-to-text product that you will be able to read and edit whenever you want. The integration of these different actions takes place in real-time, but it happens so fast that you barely notice the lag between your spoken words and the final product that appears on the screen of your device.

Up until some time back, machines were not able to work properly in noisy environments. Moreover, they were thrown off when faced with different accents and voice tones. Apart from that, most devices required a lot of time to recognize a particular voice. Sometimes, the learning curve lasted for days and mistakes were common. Luckily, all of that is in the past as both hardware and software have undergone major changes. This whole process helps us convert sound waves into numbers bits that can be easily identified by a computer system.

Inspired by the functioning of human brain, scientists developed a bunch of algorithms that are capable of taking a huge set of data, and processing that it by drawing out patterns from it to give output.

These are called Neural networks as they try to replicate how the neurons in a human brain operate. They learn by example. Neural Networks have proved to be extremely efficient by applying deep learning to recognize patterns in images, texts and speech.

Recurrent Neural networks RNN are the ones with memory that is capable of influencing the future outcomes. So RNN reads each letter with the likelihood of predicting the next letter as well. RNN saves the previous predictions in its memory to accurately make the future predictions of the spoken words.

Using RNN over traditional neural networks in preferred because the traditional neural networks work by assuming that there is no dependence of input on the output.

They do no use the memory of words used before to predict the upcoming word or portion of that word in a spoken sentence. So RNN not only enhances the efficiency of speech recognition model but also gives better results. It is the hidden memory. It stores the data of what things took place in all the previous or past time steps. It is calculated as:. It implies that by passing various inputs at different steps, the same task is being done at every step.

This limits the number of parameters to be learned. Even though there is an output at each time step, dependence on the task to perform is not required.

To make it easier to understand, consider an example where we have to predict the output of a sentence. To do so, we won't concern ourselves with the output of each word, but with the final output. Same implies for the inputs as well, that is, we do not need input at each time step.

So far, we know that in RNN, the output at a certain time step not only depends on the current time step but also on the gradients calculated in the past steps. In order to do so, you will have to back propagate 5 steps and sum up all the gradients. This method of training an RNN has one major drawback. This makes the network to depend on steps which are quite apart from each other. We know that RNN cannot process very long sequences.

They consist of cell state that allow any information to flow through it. It took decades to develop speech recognition technology , and we have yet to reach its zenith. In this article, we will outline how speech recognition technology works, and the obstacles that remain along the path of perfecting it.

At its core, speech recognition technology is the process of converting audio into text for the purpose of conversational AI and voice applications. Where we see this play out most commonly is with virtual assistants.

We speak, they interpret what we are trying to ask of them, and they respond to the best of their programmed abilities. The process begins by digitizing a recorded speech sample with ASR. The spectrograms are further divided into timesteps using the short-time Fourier transform. A contextual layer is added to help correct any potential mistakes.

Here the algorithm considers both what was said, and the likeliest next word based on its knowledge of the given language. Finally, the device will verbalize the best possible response to what it has heard and analyzed using TTS. This is the input stage. Though humans are hardwired to listen and understand, we train our entire lives to apply this natural ability to detecting patterns in one or more languages. It takes five or six years to be able to have a full conversation, and then we spend the next 15 years in school collecting more data and increasing our vocabulary.

By the time we reach adulthood, we can interpret meaning almost instantly. Businesses can also use call recordings to improve the customer experience overall—online, on the phone, and after the sale.

This site does not support Internet Explorer. Use a modern browser for an improved experience. Learn Marketing Strategies. What is Speech Recognition Software? How Does It Work?