How Speech-to-Text Conversion Works

The introduction and evolution of speech-to-text conversion software have in a great way changed how we interact with our devices. From typing replacement and aid with transcription needs, speech to text conversion is one of the magical productivity hacks of the century, thanks to technology.

In speech-to-text conversion, a voice user interface receives a speaker’s words and transmits them to a computer program that analyzes their phonemes, compares the results to a database of known words, reviews the syntax of the words received, and then prints what it believes has been spoken.

Unlike what some would anticipate, speech-to-text conversion is quite accurate having updated software score between 95-99% on average. Hence, most of us are left to wonder how speech-to-text conversion works. The aforementioned question is henceforth answered here alongside other major questions revolving around speech-to-text conversion.

How Speech-to-Text Conversion Works

While taking pride in the effectiveness of speech-to-text conversion tools, many might forget the tons of failures that came along the way to reliable speech recognition technology. Notably, speech recognition is quite a complex art that has been perfected through the ages. Data analysis, filtering, and digitizing make up the foundation of the speech-to-text conversion success story. The following are the notable steps that speech undergoes through its transformation to text.

Phase 1

The whole process of speech-to-text conversion commences with an analog-to-digital converter. Basically, this involves the detection of audio vibrations as uttered, having them converted to digital form, in most cases binary form- the language computers understand. Here background noise gets filtered out with the sound being disjointed into different frequency bands.

Also, sounds are normalized with this coming with adjusting to a constant volume and speed level. The sole purpose of this action is to have the sound templates stored in the database of the converters.

Phase 2

At this stage, the sound is quite ready for conversion. Sound signals are then split into small fragments which can be up to milliseconds in some cases. The sound fragments after this can be matched to recognized phonemes of the desired language. Notably, a phoneme refers to the smallest component of a language. You might be worried about phonemes for the millions of words that exist in the world.

However, this should not worry you. Every language has its set of phonemes which are well comprehended by the speech-to-text conversion tool. According to linguists, we have about forty to forty-four phonemes when it comes to the most popular language- English. Hence, it is easy for the speech-to-text conversion tool to analyze every word in its single components, that is, phonemes.

Phase 3

Here, the speech-to-text conversion program gets to examine the order of the phonemes as uttered. The process always involves complex mathematical models in the analysis of the words mentioned. Probably, this can be said to be word prediction, which is quite accurate thanks to the series of improvements of the speech-to-text software.

For perfection, the speech-to-text software also runs complex mathematical models through a database of known words. Even better is the comparison of words uttered to known sentences and phrases for the determination of the highest probability of the words uttered in the analyzed audio. After this, the computer gets to convert the processed data for output in text.

You might need to realize that automatic speech-to-text conversion tools begin the conversion process at phase two since they work on pre-recorded audio.

What Are the Different Models of Speech-to-Text Conversion Software?

The speech-to-text conversion software or tools can be classified based on their models. When it comes to the model classification, there are two models through which speech to text conversion tools lie, that is acoustic, linguistic, and speaker models.

The acoustic model gets to convert the audio into small acoustic units while the linguistic model gets to convert the acoustic models into words and phrases. With the acoustic model, the units are then matched to sounds that are commonly used in our daily activities.

The linguistic model on the other hand gets to analyze the relationship between the words for the determination of what word to put into application.

On the other hand, the speaker-dependent model is trained for a particular voice. Here the software can be trained to recognize your voice alone. However, you might realize that the speaker-dependent model is barely flexible hence cannot be used reliably in most of the settings.

Are All Speech-to-Text Conversion Tools the Same?

As we would expect, speech-to-text conversion tools are barely similar with each tool specialized for a given purpose. While some of the tools might be designed for repetitive work such as routine calls, others are advanced for real-time output.

The first tier of speech-to-text conversion tools relies on pattern matching hence having a limited vocabulary. In most cases, such tools are used in understanding digits and other simple speech-to-text conversion tasks.

The most common speech-to-text conversion tools rely on statistical analysis and modeling. As discussed above, their process of speech-to-text conversion involves analysis, filtering, and digitizing of audio. Finally, advanced levels of speech-to-text conversion build on the previously mentioned tools having them based on artificial neural networks.

For the advanced tools, the software has the possibility of learning and improving its vocabulary for efficient and reliable speech-to-text conversion. Such speech-to-text conversion tools are often powered by machine learning.

Potential Variables in Speech-to-Text Conversion

Machines have a long way to go in achieving what the mind of a human being can do. In fact, machines try to simulate or exercise what has already been contemplated by the mind of a human being. Hence, total correctness and accuracy at all times might be quite a tall order for speech-to-text conversion tools. With different accents, some words and details might be transcribed differently.

Notably, the advancement of the speech-to-text conversion tools has now the option to improve the vocabulary of the software hence making it easier to handle similar complexities at some other time.

So, Do We Really Need these Conversion Tools?

Other than the thrill of having software convert audio to text, there is a various area where high-quality transcription is required to perform profession-related activities. While speech-to-text tools are not as accurate as human beings, they offer efficiency and work rate turnover that humans can barely achieve hence making them indispensable.

After all, not many of us are ready to do some labor-intensive and time-consuming transcription services. However, some human touch on the transcribed tasks comes in handy for the perfection of the transcribed work.

What is the Current State of Speech-to-Text Technology?

With the recent advancement of speech-to-text conversion technology, life has been made convenient in transcription. This has been depicted by the wide range of use of the open opportunities created by speech-to-text conversion tools. From improved healthcare, customer services, journalism, to qualitative research, more and more industries have taken into consideration having some reliable speech-to-text conversion tools for efficiency and increased productivity.

What is the Future of Speech-to-Text Conversion?

From governments to private corporations, there has been myriad research and development on the art of speech to text conversion. The Defense Advanced Research Projects Agency in the United States has been the most notable pillar in the research on some ambitious plan, GALE- the Global Autonomous Language Exploitation program. With GALE one can translate two languages instantly with an accuracy of over ninety percent.

Additionally, DARPA has also funded TRANSTAC a project aiming at understanding how soldiers can conduct effective communication with people in non-English speaking communities. This shows the agency’s ultimate dream in the development of a universal translator for all popular languages.

Benefits of Speech-to-Text Converters

There are myriad benefits that come with adopting speech-to-text converters for any of your transcribing needs. Some of them include.

  • Speed

While humans might offer the best transcription services, they can barely beat speech-to-text conversion tools in speed. Lengthy audios can be transcribed in minutes. Optimizing the rate of output and quality of transcriptions, it is evident that speech-to-text conversion tools edge out human beings by far.

  • Boosts productivity

Let’s be honest! Manual typing is time-consuming and tedious work. With speech-to-text converters offering to do this, one can handle other pending tasks hence improving productivity which for an enterprise translates to better profitability.

  • Convenience

The use of speech-to-text converters offers great convenience thanks to the provision of typing alternatives. Enterprises have their meetings transcribed in real-time hence requiring no extra time for tedious manual transcription which is both tedious and time-consuming.

Limitations of Speech-to-Text Converters

While the speech-to-text converters might offer quite some service in transcription needs, there are major limitations that are yet to be overcome. Major limitations include.

  • Understanding homonyms

With homonyms comes different words with similar phonemes but different meanings and spelling. For example, a speech-to-text converter might not really put across clearly the difference between fare and fair during a transcription. Even though artificial intelligence has tried to cover this limitation, it hasn’t been fully handled.

  • Recognition of some accents and dialects

While the English language is definite, we all articulate the words and phrases differently. Hence, we expect the endless tally of accents and dialects to pose more troubles for the speech-to-text converters. However, with the library for the converters growing from time to time, there might be success and improvement noted at each time.

  • Recording conditions

The efficacy of speech-to-text converters largely depends on the recording conditions. When audios are made in noisy places, or many people utter words or phrases at the same time, this will always affect the output offered by the converters.


We can never take for granted the immense and reputable work that speech-to-text converters have done over the few years. While they have been marred with some limitations, speech-to-text converters can only get better with every dawn. Coupling smart modeling of software, balanced data, and scenario exploitation speech to text converters can be made even more efficient.

Gene Botkin

Gene is a graduate student in cybersecurity and AI at the Missouri University of Science and Technology. Ongoing philosophy and theology student.

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