To transcribe audio into text means to translate it from an audio format into a written one. This technique is used wherever accurate speech reproduction is required: in education, journalism, when keeping minutes of meetings and business meetings, to create content for the hard of hearing.
Previously, verbatim recordings of conversations were conducted using shorthand. Now technology has come to the rescue. At first, the fixation process was facilitated by the introduction of sound recording devices. Later, innovative AI-based solutions took over transcription, for example, the creation of subtitles in real time. The article discusses issues related to the process of automatic speech recognition, highlights the advantages and disadvantages of some of the most common services.
Principles of neural network transcription
Sound is a digital stream. A dataset is a structured data set where each object has prescribed characteristics, properties, connections, and locations. Datasets are widely used in training neural networks. In our case, datasets are a set of data with a text interpretation. With the help of training, the neural network learns to recognize speech sounds in accordance with the text transcripts of datasets. The neural network establishes and remembers the correspondence of certain spectrograms of an audio recording with symbols. In the process of transcribing the voice into text, the audio file is divided into short segments representing a certain pattern. Such graphic drawings look different for different languages. The neural network remembers such drawings and over time begins to distinguish between speeches spoken in different languages.
After recognizing the neural network, it is necessary to translate the resulting drawing into plain text. To do this, she uses a decoder, a tool with a list of words from which to choose the appropriate one. As a result of long-term training, the system learns to select the words, phrases, and expressions that are most appropriate for the context. By replacing a set of words in the decoder with foreign ones, you can teach the system to transcribe an audio file with foreign speech in the appropriate language. In addition, modern algorithms allow you to teach a machine to use a personal context. For example, if the user’s phone has a list of contacts with names, you can teach the network to send messages with the name, for example, «Transfer 100 rubles to Vasya».
For transcription, noise, indistinct diction, speed and volume of conversation are still a significant problem. The quieter and dirtier the audio recording, the worse the quality of the recognized speech will be.
Advantages and disadvantages of automatic transcription
Among the advantages of transcribing speech into text:
- high processing speed (transcription of the average text takes seconds);
- the ability to transcribe in real time, which is often used to create subtitles for videos;
- no volume restrictions;
- the ability to work with both streams and records;
- API integration: you can establish the interaction of your software with a neural network service synchronously, asynchronously or in streaming mode;
- simplify tasks for those who work with large amounts of information from different sources that need to be stored in text format.
Among the disadvantages:
- high requirements for clarity, sound clarity, and playback speed (too fast, fuzzy diction will lead to an increase in recognition errors);
- limitations of the perception of complex terminology;
- errors in recognizing non-standard turns of speech.
Even the most advanced neural networks cannot yet perfectly transform audio into text format. They only reduce the time for transcribing audio recordings into text, simplify the listener’s work, but still require human correction.
How to choose a transcription method and service
There are several types of transcription:
- automatic;
- semi-automatic;
- professional with the involvement of a specialist decryptor.
Automatic recognition means that all the work on translating audio into text format is done by a machine. It fully meets the request for how to quickly transcribe audio into text. Many companies provide their developments for free. The built-in software can recognize and transform speech in real time. This type of transcription is suitable for creating subtitles for videos, for journalists doing interviews, for students recording lectures.
However, the quality of free services is very low. Recognition accuracy decreases dramatically when noise appears, if the voice is quiet or indistinct. Algorithms, as a rule, do not distinguish between the voices of several speakers, when translating audio into text, they make many grammatical errors, and incorrectly select words.
The semi–automatic method is a combination of machine and manual treatments. The quality of the finished product is significantly higher, since more advanced programs based on well-trained neural networks are used for recognition. Speed, accuracy, and literacy are higher here. You will still have to verify the written document, but it will not take much time, since the machine will do most of the work well.
However, you will have to pay for using additional features. For example, the advanced iOS app Whisper from OpenAI automatically detects the language, quickly and efficiently transcribes, punctuates, and highlights paragraphs. The monthly cost is $ 10. The Scribe service provides the possibility of free high-speed transformation for only 10 minutes, then the speed decreases sharply as users are queued. For high speed and no queue, you have to pay 1,290 rubles for 5 hours. In this case, the neural network will spend several seconds or minutes (depending on the volume) on creating a written document instead of several hours, while punctuating punctuation marks and timecodes. By the way, the «Scribe» recognizes the dialect of up to 5 speakers.
The semi-automatic method can be used in business during meetings, meetings, journalism, medicine.
Professional means the work of a professional transcriber who, listening to a recording from a dictaphone or other source, accurately and quickly transcribes using the keyboard. The services of professional decryption specialists are usually resorted to when the accuracy of the received text is most important or confidentiality must be respected.
When choosing a service, you should pay attention to:
- speech recognition accuracy;
- confidentiality guarantees from the manufacturer;
- The speed of transformation;
- additional options, if necessary (for example, analytics, editing, text search);
- cost.
AI secretary from FollowUp for transcription
Using the tool guarantees:
- high decryption speed with an accuracy of at least 98%;
- complete confidentiality;
- absolute preservation of all important details of the negotiations;
- timely distribution of Sammari to all interested parties.
In addition, the AI secretary is trained to conduct analytical research, for example, he can analyze the content of a meeting with elements of negotiations and make recommendations on improving the principles of communication with the client. In the field of recruiting, a neural network assistant can tell you how best to interview candidates in order to more accurately determine their aptitude, as well as identify the weaknesses of the applicant and give recommendations for deeper verification.
The service has already been successfully operating in industries such as:
- trading;
- education;
- designing;
- consulting;
- recruiting;
- marketing;
- management.
If necessary, FollowUp engineers will refine the service to meet the needs of your company, as well as help with its integration.
The service facilitates the work of the staff, reducing the time to complete the routine and helping to complete the tasks on time, as well as the manager. For the latter, the AI secretary becomes an assistant, allowing you to monitor the production process, optimize business tasks, constantly increasing the efficiency of the company.
Conclusion
As we have seen, transcribing audio into text is not such a difficult problem today. High-tech neural networks for automatic recognition of audio recordings are becoming reliable and indispensable assistants in all areas where high-speed and accurate processing of large volumes of audio information is required. Despite the errors in the proposed solutions so far, their introduction into production significantly simplifies the work of both personnel and management, increases the efficiency of work processes, and improves internal and external communication. And to make the effect of innovation tangible, choose services with a wide range of options.
