How Speech to Text Software Works

At times, it truly does feel like we are living in a futuristic society.

Technology has achieved remarkable feats and we find ourselves becoming used to having our devices and computers handle a lot of our workload for us.

With companies like Apple, Amazon, Google, etc,. We can very nearly speak to any device in our homes.

These devices can understand speech and transfer it to search data, commands, or messages.

We can literally witness a machine take out speech from an audio and convert it into usable text.

But, how does this work exactly?

What Is Speech To Text?

Speech to text is essentially a computer generated program that is designed to recognize speech patterns and convert them into text.

This is a form of machine learning that has made huge strides in the past decade.

It works by breaking down the audio into two basic models: Acoustic and linguistic.

Two women having a conversation while recording their conversation


The acoustic model is when the audio is entered into the software in its purely acoustic form.

This is when the program extracts the ‘phonemes’ from the audio and converts that into data that can be analyzed.

A phoneme is the vibration made by sounds that can be converted into speech.

We recognize certain sound patterns as words, and this is how language is born.

The acoustic model in speech to text analyzes this information and breaks it down into its raw acoustic waveform–those wavy lines you see when you look at a music file while editing it.

By analyzing the acoustic portion of an audio, the machine can begin to associate certain phonemes with various sounds–I.e words.

However, this doesn’t solve the issue of certain words that sound like others but are in fact very different.


The linguistic model is when the program takes this waveform and begins to piece it together into text.

This is done through advanced machine learning that has been perfected over time.

The program will begin to analyze the phonemes being used in the audio and associate them with words.

Based on the known language, sentence structure and context clues, the machine can then organize these sounds into full sentences, paragraphs, and more.

This is the area of speech to text development that has been the most difficult to ‘get right’.

But modern machine learning has gotten amazing at being able to predict and transcribe full text from audio, with proper punctuation, fairly accurately.

The ‘Speaker’ Model

One newer aspect of speech to text development is the third model: The ‘speaker’ model.

This model is training programs to identify tones in voice, as well as dialects and speech patterns.

This allows the program to identify specific speakers and isolate their voice.

This helps with ensuring personal devices like your phone don’t respond to commands from anyone else beside you.

It also helps machines to transcribe and analyze audio that features more than one speaker.

These are respectively known as ‘speaker-dependent’ and ‘speaker-independent’ models.

Why is Speech to Text Development Important?

The primary reason for this is accessibility and convenience.

Being able to have a machine transcribe and recognize speech can allow anyone–both hearing and visually impaired–to utilize computers and access higher learning.

This breaks down barriers in the workplace, in education, and more.

These programs can also help us to better understand each other.

By developing powerful speech to text and speech recognition A.I we can help to break down language barriers, dialects, and cultural differences.

Making understanding each other easier is the ultimate goal of these programs.

But they can also be used as a utility.

Speech to text programs allow for automatic accurate transcription.

This allows doctors, lawyers, students, and all of us to take notes of our recorded thoughts, and that’s just the tip of the iceberg.

So, it’s pretty easy to see why development in this area of machine learning is so useful.

Speech to text programs can open so many doors for our society as a whole.

Knowing the Difference

Not all speech to text programs are created to serve the same purpose.

As we mentioned above, these programs are highly advanced–but they are also incredibly versatile.

So, what sets them apart from each other?

Well, there are a whole host of different programs that are designed to perform different functions.

Some are designed to recognize speech and translate that into search queries, I.e Google Assistant, Siri, Alexa, etc,.

Other programs are designed to take audio and convert the speech into text files and transcriptions.

That is what we do here at Focus on Listening.

We allow users like you to have your audio files converted into text with over 95% accuracy through our advanced programs designed to identify precise speech patterns.

This is the difference between speech to text programs designed for transcription, and those designed for computing and search inquiries/commands.

Both are useful, but both serve very different purposes in society.

Final Thoughts

Understanding how speech to text programs work will allow you to make the most informed decision when it comes to utilizing these types of services.

Are you ready to see how far machine learning has come with our advanced speech to text program here at Focus on Listening?

Let’s get started!

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