Transcription of audio into text: features and useful services for decoding

Transcription is the translation of audio and video recordings into text. For example, you recorded an interview on a dictaphone, and now you need to write an article from the received material. Previously, you would have to scroll through an audio recording dozens of times to manually record the interview text. This can now be done using trained artificial intelligence. It is enough for the user to place the file in the working field of a special program, and the machine will decrypt the audio or video and output the result in text format.

Transcription and the history of its development

Transcribing previously recorded speech is not a new technique. Even before the advent of sound recording technology, there were specialists involved in transcription, but they were called stenographers by another name. They were professionals who knew a special language for writing texts using signs. This method made it possible to speed up the recording of the speaker’s speech several times, while preserving the full text. Then the transcript was deciphered and the text was typed on a typewriter.

The transcription method was invented in the middle of the last century, but its widespread use became possible only in the early noughties, when machine learning technology appeared.

The advent of Speech-to-Text or STT technology has greatly facilitated the work of those whose occupation involves processing large amounts of audio or video. The robots took on the most difficult task. They have learned to recognize words, put together sentences from them, form extracts (Sammari) and much more, and they do it ten times faster than a human. Nevertheless, machine translation is still far from perfect. The recognition quality of many programs is not high enough, not all neural networks are able to edit and format recognized material, simpler programs make many spelling and semantic errors.

For this reason, people are still in no hurry to completely abandon manual transcription. Converting audio to text format manually takes much longer, and the service is expensive. But in some cases it is preferable to use this method. A person is able to hear even an indistinct audio recording, he will not be confused by an accent or poor diction. In addition, manual conversion may be necessary if the essence of negotiations and meetings must be kept secret.

Nevertheless, automatic conversion of media files to text format is increasingly being used in various sectors of the economy, in education, healthcare, and law. A great achievement is the AI’s ability to recognize foreign speech. This, on the one hand, allows you to collect additional information on a topic of interest, and on the other, to expand the audience by attracting foreign listeners (viewers).

How audio-to-text translation works

Words consist of letters, which (with rare exceptions) denote sounds pronounced orally. The sound in modern devices is converted into a digital stream, which the programs then work with. Each sound or combination of sounds forms a unique, but at the same time characteristic pattern on the spectrogram, which is studied and analyzed by neural networks. Learning AI to recognize sounds and letters can be compared to putting together puzzles. He is offered datasets (an analog of educational material) in which audio recording and text transcription are combined, and the machine is trained to compare each drawing left by the current sound with the image of sounds in the sample datasets.

In this way, the machine learns the alphabet, learns to make syllables out of letters, and words out of syllables. The text that needs to be recognized is divided into micro-slices (phonemes), and artificial intelligence begins the calculation process: which sound, syllable or letter this phoneme is most suitable for.

Words are defined according to the same principle. Having identified the syllables, the neural network turns to the dictionary and finds the most suitable words in it. Adding sentences is a bit more complicated. Here, the selection is not only based on the recognized words, but is often selected according to the context. For example, if the question arises as to which verb to choose: “go” or “ride”, the robot will analyze which words are nearby. If it is “pedestrian”, then he will choose the word “walk”. If there are words for vehicles nearby, then the choice will be made in favor of the verb “to go.”

Most neural networks can self-learn. If the user frequently uses certain speech patterns, words, or names, the robot will eventually learn to recognize and use them correctly.

AI can transcribe speech only in the language in which it was taught, since all languages have different alphabets, speech constructions, and words sound with a certain pronunciation.

What tasks can be solved by transcribing audio and video into text?

Business

The use of interactive voice systems (IVR) allows you to establish contact with the customer and clarify his wishes. By asking pre-recorded questions, the robot will find out the customer’s preferences and select the appropriate answer. This manipulation will simplify the manager’s communication with the client, because when it comes to live communication, the manager will already have an idea of how to negotiate.

The introduction of voice recognition technology in telephony helps the company to improve the quality of service. It is easier to analyze negotiations recognized by the neural network, identifying violations, incorrect behavior of the parties, and establish more effective negotiation techniques.

With the help of a voice assistant, which also operates on the basis of speech recognition technology, entrepreneurs have the opportunity to record meetings, meetings, meetings, negotiations. Artificial intelligence has learned to record, convert, highlight the main thing, record agreements, responsible persons, and deadlines for completing tasks, freeing secretaries from the exhausting routine.

In addition, using automatic voice recognition technology, you can:

• conduct marketing research (surveys, review analysis, market changes);
• create promotional video content for blogs and social networks, for example with subtitles, to expand the audience at the expense of the hard of hearing;
• automatically generate the client database;
• to conduct the first stage of recruiting in order to cut off obviously unsuitable candidates.

Journalism

For media workers, the correctness of transmitting information about events (with dates, logic of presentation, facts, especially when it comes to interviews), as well as the speed of material submission to the press, is of great importance. Now, with a gadget with neural network assistants, journalists do their job much faster and more accurately. The robots decode and translate speakers’ speeches at congresses, forums, and conferences for hours in a matter of minutes. All that remains for a person to do is re–read, correct errors, and format.

Education, medicine, law

These three areas of social and social life use technology in approximately the same way. Teachers, students, doctors, lawyers are associated with the need to keep records in large volumes:

• teachers prepare lecture materials;
• students should have time to take notes on everything;
• Doctors are required to carefully fill out medical records and keep patient cards.;
• During court sessions, lawyers are required to record the progress of the case and the speeches of the participants in all details.

For people in these fields, the introduction of neural network assistants that understand human speech has significantly reduced the workload associated with writing, allowing them to focus on more important issues.

In everyday life

The possibility of voice control is widely used in everyday life. A person giving voice commands can:

• search for information on the Web (choose music, movies, articles);
• make bank transfers;
• manage a number of functions in the car (set the address to the navigator);
• create video content with subtitles.

Advantages and disadvantages of using STT

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