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Speech-to-Text: what is it, what is it for, who is it for?

October 27, 2021

With accuracy rates now exceeding 99%, Speech-to-Text solutions are the new frontier for businesses looking for new ways to improve their productivity and deliver satisfying experiences to their employees and partners. With a keen and necessary eye on accessibility.

When we talk about Speech-to-Text (STT), we are stating an assistive technology that is able to ‘translate’ audio content into written words, converting it into either a text document or another display mode.

Originally, speech-to-text software was designed specifically for desktop environments, but the growing popularity of mobile devices and the boom of apps have convinced developers and independent software vendors for the need to make these types of applications and functionalities available also on smartphones and tablets, thus opening up their use in an increasingly wide variety of application scenarios, ranging from education to business.

Speech-to-Text: A definition

Speech-to-Text is referred to an interdisciplinary technology, which combines computer science, engineering, and computational linguistics to enable the recognition and translation of spoken language into written text by computers. 

This is not a new technology, the first experiments date back to the 1970s, but there is no doubt that in recent years developments in the field of big data and artificial intelligence have given a strong boost to the improvement of this technology and consequently to its reliability. 

Compared to the past, transcription accuracy has actually improved to such a degree that, with a clear and well-defined audio source, the accuracy rate can exceed 99%.

This point must be underlined as a lot depends on the recording conditions. Speech recognition software still struggles to interpret speech in a noisy environment or when many people are talking at once. 

It is also the environmental conditions, more than the complexity of the speech, that can determine the quality and accuracy of the transcription.

How does Speech-to-Text work?

The core of an automatic transcription system is automatic speech recognition, which integrates acoustic and linguistic components. The acoustic component is responsible for converting audio files into a sequence of very small acoustic units. The “analogue sound” i.e., the vibrations created when speaking, is converted into digital signals that can be scanned by the software. The acoustic units are then associated with existing ‘phonemes’ i.e., the sounds used in a specific language to form meaningful expressions. Then the linguistic component is responsible for converting the sequences of acoustic units into words, sentences, and paragraphs. The linguistic component analyses all preceding words and their relationship in order to estimate the probability of using one or another word in the continuation of speech. Technically they are called ‘Hidden Markov Models’ and are widely used in all speech recognition software. Both components must be adequately ‘trained’ to understand a specific language: equally the acoustic and the linguistic part are crucial for the accuracy of the transcription. But that’s not all.

When talking about voice recognition solutions, we can mention two specific models: speaker dependent or speaker independent. In the first case, the model is trained on a specific item and specific use cases. A higher accuracy in the result is guaranteed, but the time needed to train the system can be long. The ‘price to pay’ for this increased accuracy is the reduced agility of the solution, which cannot be used in other contexts. In the speaker independent model however, the system is able to work with several different voices without requiring any specific training.

Which applications are for Speech-to-Text?

Given these considerations, let’s try to understand which are the most interesting applications of transcription systems, focusing in particular on the specific declinations for the world of business and enterprises. There is no doubt that in a scenario where companies are looking for new ways to introduce efficiency, automating repetitive and low value-added tasks where possible, Speech-to-Text solutions find their perfect fit. The typing speed of a voice system is not comparable to manual typing, which makes it much easier for employees to take notes and minutes with only a fraction of their time spent on transcribing them into their final form. This is all the truer when it comes to transcribing reports or minutes of meetings: it is not only time that is at stake here, but also the related responsibilities. Implementing a text-to-speech solution not only reduces the time needed to prepare notes and summary documents, but above all, allows all meeting participants to take a more active and constructive role in the discussion without having to worry about taking proper notes of each speech. 

This has an additional benefit, which is not always negligible.

Often the minutes of meetings are written in a very dry and essential style. The availability of an exact transcription of what has been said can help to produce a more authentic and engaging summary document for those who read it.

The benefits on productivity and efficiency are not only found in meetings. 

Speech-to-Text software also support workers on the move. Although it is not recommended to take notes while driving, speech transcription software allows you to record notes, summarise the highlights of a meeting, create a to-do-list or recap a brainstorming session while travelling, all with comparable or better accuracy than humans. This, as part of a collaboration scenario, is what we have become increasingly accustomed to over the past year, makes it easier to share the content of phone calls or informal meetings with employees, collaborators or members of a working group.

Speech-to-Text, between health and accessibility

There are other benefits worth mentioning.

One relates to the health and experience of workers. 

By reducing the time spent typing, Speech-to-Text solutions ease working conditions that are often the cause of illness or discomfort, such as carpal tunnel syndrome or eyestrain. At the same time, allowing employees to work on the most valuable aspects of voice transcription can be a form of fulfilment and relief from overly routine tasks. 

The second aspect is related to accessibility. 

Integration of speech synthesis technology into business operations is a step towards greater accessibility. People who have difficulty typing that use conventional input methods find Speech-to-Text as an answer to their needs. If well integrated into the corporate infrastructure, Speech-to-Text is an important pillar that allows employees and collaborators to choose the digital input method that best suits their needs. Moreover, it is crucial to remember that digital accessibility is one of the commitments required, at least from Governments, by EU Directive 2016/2102: podcasts, videos, and audio recordings must be provided with captions or transcripts to be accessible to people with hearing impairment. Why shouldn’t businesses adopt this too?

The next frontier of Speech-to-Text

While much has been done in recent years to ensure that voice transcription systems are able not only to reach new levels of accuracy but also to understand specific languages of sectors or vertical fields, there are new frontiers to work on.
The first is called Natural Language Understanding (NLU). This is a branch of Artificial Intelligence, which explores the possibility of understanding and interpreting human language by machines.

Applied to voice recognition technology, NLU enables not only the transcription of human language but also the understanding of its meaning. 

This opens up a world of opportunities to be explored, from machine translation solutions to the creation of summaries of articles, essays and documents, from the possibility of classifying content to sentiment analysis similar to what is already happening on social media.

It is a not-so-distant future, in which many are already actively working on.