Speech recognition technology has become firmly established in the everyday life of ordinary users and has long been unsurprising. Even elementary school students can do Google searches using their voice. Voice assistants have become as common in everyday life and business as phones or televisions. The innovation saves time, allowing you not to be distracted by the routine tasks of turning on and off devices, searching, and analyzing information.
The introduction of voice-to-text conversion technology has opened up new opportunities for journalists and those involved in the education system. Now students, teachers, and media workers don’t have to spend nights deciphering recordings made in a hurry and worrying about mistakes.
The neural network will record everything, recognize it, and print it in a few minutes.
The technique of voice processing was invented more than 70 years ago. However, high-quality processing of human speech became possible only at the beginning of this century due to the development of machine learning. This is how the Speech-to-Text or STT technology appeared. Her popularity is growing rapidly. Just a year ago, 25% of companies already used it. Analysts predict that by the end of this year, the speech technology market will triple and reach a value of $26.8 billion.
How does audio content recognition technology work?
Transcription is a complex multi–stage algorithm based on artificial intelligence. The neural network is responsible for processing, recognizing, and converting audio to text.
Human speech consists of sentences, sentences of words, words of letters, letters of sounds. During playback, each sound leaves a unique pattern on the spectrogram of the audio recording. Engineers are preparing special datasets for the neural network (in other words, training samples), each of which consists of a voice recording and an accompanying marked-up text. The AI is offered an audio recording-text pair, and it must recognize the patterns of each sound, then assemble letters from the sounds, and words from the letters.
After receiving the task (dataset), the machine splits the content into short sound segments – phonemes – and begins to analyze, trying to calculate the most appropriate ones: The AI does not give a single accurate answer, but a set of several most appropriate letters.
When the sounds and letters are roughly defined, the machine begins to select word variants. She turns to the context (dictionary): compares the sets of letters that she has recognized with the sets found in the dictionary, and thus calculates the appropriate words.
Now you need to assemble sentences with the correct meaning from the words. The better the system is trained, the more accurate the transcription result will be. The training depends on the number of texts that were recognized during the learning process. However, advanced neural networks are able not only to remember what they once recognized, but also to self-learn by drawing conclusions and remembering the various nuances they encountered.
For example, the neural network recognized something related to movement and chose two options: “go” and “ride”. When making a meaningful sentence, she will focus on neighboring words. For example, if the words “pedestrian” and “sidewalk” are nearby, the AI will choose “walk” because pedestrians cannot drive, they walk. But if she finds the word transport, car, cart, she will choose “go” because she “knows” that vehicles do not walk, but drive.
The stages of transcription
- Recording audio material.
- Analysis. After making an audio recording, the system sends it to the server, where it is cleared of noise and divided into microfragments – phonemes with a length of 25 ms. Each phoneme is passed through an acoustic model to identify the spoken sounds.
- Decoding. Similarly to the definition of sounds, syllables and words are defined: the system again turns to the acoustic model, identifies similarities, selects words, and determines their meaning.
- Convert speech to text. Using a language model, the machine forms a sentence, and selects the unrecognized parts of the sentence according to the context.
- Decoding. The recognized text is transmitted to the decoder, which combines the data of the acoustic and speech models and transforms them.
Recognition quality, other languages
Artificial intelligence recognizes audio content only in the language it was taught in. For example, if the training took place in Russian, then she will not understand English (a different alphabet, other syllables, speech constructions). More precisely, she will hear the sounds, select something most suitable from the acoustic and language models, but in fact it will turn out to be unreadable letter sets. In other words, in order for the machine to handle transcription in different languages, the learning process must also be organized in different languages.
Modern sound recorders are highly sensitive, so they are able to make good audio recordings, even if there is sound interference in the audience. But still, the smaller the initial background, the better the recording will be, which means that the transcription will be more accurate. The speaker’s gender and age have no effect on the AI’s performance. But intonation, emotions, pronunciation, features of articulation, semantic content (for example, linguistics and intonation of fairy tales and news are very different) can create difficulties. These aspects should be taken into account when training artificial intelligence. Thus, the better the training materials are selected, as well as the more tasks on various topics the machine performs in the learning process, the better it will work.
The application of innovation in various fields
As mentioned above, a quarter of companies use STT to a greater or lesser extent. With their help, they have automated many production processes, increased the level of internal communication, labor productivity, and the quality of customer interaction. Speech-to-Text has penetrated into medicine, law, education, and healthcare, and is also increasingly being used in everyday life.
Business
- Interactive voice systems (IVR) allow you to communicate with the client, determine his wishes before the person calls the operator. A potential buyer dials the company’s number, and on the other end of the line, the robot asks what the person is interested in. After waiting for the answer and writing it down, the machine selects the appropriate answer. If the robot can’t find anything for the request, it will ask you to reformulate the request. The innovation reduced the number of lost calls, which were numerous when using voice menu technology. It was difficult for many people to remember which questions to press on which button.
- Research of consumer requests by telephone survey. Instead of a clerk, a robot is engaged in calling customers or random subscribers. It automatically dials numbers, asks the same question, and records the answer.
- Analysis of telephone conversations with the client. A manager who works with potential customers over the phone must strictly follow the conversation pattern (greeting, introduction, setting the purpose of the call, identifying wishes). In addition, it is important to maintain polite and correct communication, even in the case of aggression from the caller. Conversations are recorded, and then they are selectively checked by a supervisor. His task is to identify the violations that led to the loss of the client. Previously, supervisors were unable to check all calls, so they did it selectively, covering no more than 25% of negotiations per day. Now, instead of a supervisor, negotiations can be monitored using IVR. The system monitors whether all points of the conversation plan have been followed, in what tone the conversation took place, and what its results are. Using the material collected by AI, it is possible to identify which negotiation techniques of the manager are more effective and led to increased sales, and which turned out to be useless.
- Automation of work. The installation of the CRM platform allows you to automatically create a customer database by recording the phone number, first name, last name, address, and then, in the process of communication, replenish the card with information about his preferences and wishes.
- Marketing research. Some platforms allow you to find out which competitors customers are more likely to compare your product with. To do this, create tags mentioning a competitor, and then analyze the speech to identify what can be improved. Another example is when a robot calls a customer back after completing a transaction and asks them to evaluate the product, delivery speed, and other parameters that can be optimized.
- Recruiting. At the initial stage of selection, the conversation with candidates can be shifted to AI. He will ask applicants basic questions and screen out those who definitely do not fit.
- AI has become an indispensable assistant for recording and transcribing meetings, meetings, and negotiations. He records the dialogues, transcribes them, compiles a sammari, and sends it to the participants.
Medicine
STT for the medical sector requires special training, as the terminology used by doctors is very different from ordinary conversation. The introduction of STT-based assistant programs has greatly facilitated the work of doctors, expanded the possibilities of medicine, and improved the quality of services provided. Artificial intelligence, which understands human speech, has taken over:
- filling out medical records (dictated by the doctor during the appointment), unloading doctors, as well as the average medical staff (Voice2Med system);
- recording and analyzing conversations with patients, which allowed us to obtain a more accurate picture of their condition, which means that it is more accurate to make a diagnosis and prescribe treatment.;
- the functions of making an appointment with a doctor, distributing patient flows so that queues do not form.
The advent of STT has become an impetus for the development of telemedicine. More and more medical institutions are connecting to the EHR system (electronic medical records), which allows doctors to quickly obtain information about the patient and apply remote treatment methods.
Daily use
- For voice control of the Smart Home system from Sber.
- To enter messages in messengers by dictation. The latest generation of AI types recognized audio without spelling or punctuation errors.
- To search for information on the Web (music, movies, broadcasts, articles) or order services on taxi servers, food.
- Voice communication with the navigator.
- Communicate with voice assistants in organizations, such as banks, to search for information and make quick transfers.
- Bloggers who create video content use the innovation to compose subtitles.
Advantages and challenges
| + | – |
| Accuracy and reliability: New generation STT technologies recognize and transcribe speech with a high degree of accuracy (up to 98%). | Technical accuracy. Platforms for use in the fields of medicine, law, and engineering require special training methods for neural networks, since there are too many specific vocabulary in these areas. |
| Effectiveness. Neural networks can process volumes of information much larger than even a few people can handle. At the same time, they spend much less time on this. | Transcription accuracy can be greatly reduced if the recording is made in a noisy environment. |
| Effectiveness. Using AI allows you not to spend money on hiring professional transcribers. | The introduction of innovations by a company is often met with concern by the team, because people are afraid of losing their jobs or not being able to cope with the innovation, so they will have to work with the team additionally. |
| Improvement of communication in the company, coordination of actions of departments, which leads to an increase in labor productivity. | Difficulties of mastering for the company’s staff. In order for advanced versions of the software to work effectively, the staff must be specially trained. |
| Relieving staff from routine duties related to filling out documentation, maintaining file cabinets, and processing protocols. | Confidentiality. Since information is transmitted to a third-party server during decryption, the risk of information leakage is always present. |
| Expanding opportunities to explore the market, competitors, and new customer needs. | |
| Facilitating the work of people who need to process large amounts of information on a daily basis (media workers, students, teachers). |
Cloud services and transcription platforms
| Name | Advantages | Number of languages | Is free | For a fee, one dollar |
| FollowUP | Transcribes the conversation; records tasks, deadlines, responsible persons, and agreements; compiles and distributes sammari | Russian | 100 minutes | flexible tariff schedule depending on the number of minutes |
| Sonix | Automatic identification of speakers; editing and formatting functions for recognized content; integration with Zoom. | 50 | 30 min. | 10/hour;22/month. |
| Rev | It is suitable for working with large volumes of audio data.; can create a dictionary for specific terms; improved recognition accuracy for highly specialized content. | 36 | no | 1.5/min |
| Riverside | There are noise reduction tools, as well as sound quality enhancement.; Allows you to edit text with automatic synchronization with audio and video files. | 101 | no | from 15/month. |
| Whisper | It is effective in difficult acoustic conditions.; handles long audio files well.; Creates subtitles for videos; It can be used to transcribe audio content of any complexity using professional terminology (during the learning process, the neural network decoded 680 thousand hours of audio in different languages). | 97 | Open source | no |
| Dragon Professional | Voice-controlled (calling the app, poisoning messages); can work with audio from the legal, medical, or educational fields.; Recognition accuracy 99%; optimized to work on Windows 10 and 11; | English; German; French; italian; spanish; Dutch | 7 days | from 15/month. |
| Descript | An app for bloggers, podcast hosts, and YouTube channels. Transcribes the voice into a written format, allows you to edit the video. | 25 | 7 days on Pro | 12/month. for the Creator version; 24/month per Pro |
| Scribe Express | One of the best programs for transcription. Dictate the message into the microphone and receive the text version. | Russian;English | yes | upgrade for a small fee |
| Dictation IO | A platform for creating letters, documents, and emails without the need for printing. It works as a speech converter on the website. | 100 | yes | no |
| Happy Scribe | Converts audio files online. | 120 | yes | no |
Advantages of cloud solutions
To ensure high-quality and uninterrupted operation of multifunctional STT platforms, companies will need:
- powerful servers;
- expensive software;
- experts for debugging.
If you try to make your own program, it will also require creating conditions. You will also need:
- servers with high computing power;
- arrays of reference sounds;
- tools for learning.
You can simplify the task by using cloud solutions, for example, the Cloud Voice platform in VK Cloud with the built-in Voice ASR tool, which is equally good at processing single files or audio streams, supports basic audio formats, and also helps:
- integrate the voice assistant;
- monitor the quality of call processing;
- use voice commands (they need to be configured).
The service is paid, but you only need to pay for the number of characters when voicing the text or the minutes spent on decoding.
Conclusion
Speech recognition technology appeared more than 70 years ago, but the possibility of its widespread use became possible only a decade and a half ago with the development of machine learning. In a short period of innovation, it has firmly entered the working and household environment. In business, STT allows:
- optimize your work;
- improve communication within the company, as well as with customers;
- automate many processes, saving staff from the exhausting routine.;
- improve the quality of service, which means conversion;
- improve the quality of marketing research.
In everyday life, innovation has made it easier to find information and manage household appliances.
The demand for technology is steadily growing. Obviously, its functionality will only expand, which will allow it to capture new areas of the economy and public life, and users will get even more opportunities.
