Are you tired of spending hours trying to transcribe audio recordings? Are you looking for a quick and easy way to convert your audio recordings to text? Look no further than the AI-powered Notta.ai website.
What is Notta?
Notta is a transcription service that uses AI technology to convert voice into text and supports 104 languages. By simply recording audio and uploading the file, users can receive automated transcription, making the process of creating meeting minutes or interview articles much easier. Notta works on standard devices such as PCs, smartphones and tablets, making it accessible anywhere. Additionally, Notta ensures the security of user data by encrypting it and providing a safe storage space for important and confidential transcripts. With over 200,000 registered users as of September 1, 2021, Notta values the power of one's voice and strives to promote productivity in the workplace.
Is Notta Free?
Yes. You may use Notta for free.
They offer a 3-day free trial for all new users who sign in Notta mobile app. In these 3 days, you can transcribe up to 120 minutes and enjoy all the great features in Notta Pro to see how our platform works for you.
Then, on the 4th day, you will automatically switch to free Notta Basic and get 120 transcribing minutes each month for limited features if you don’t subscribe to any Notta plan.
Note: If you sign up for Notta on Notta web, you will directly start free Notta Basic for free and get 120 transcribing minutes each month for limited features.
How does Notta work?
Speech recognition is a technology that has been improved over half a century to convert spoken words into text. It is used for various applications like voice assistants, transcriptions, and voice biometrics. Technologies such as deep learning and big data are used for speech recognition. The initial steps of speech to text involve converting speech to digital format, audio classification, and spectrogram analysis. The speech-to-text software utilizes linguistic algorithms to categorize auditory signals of speech and convert them into Unicode. The process involves analog to digital conversion, filtering, segmentation, character integration, and the final transcript. Custom speech-to-text solutions can increase transcription accuracy, which makes it useful across industries.
1- Analog To Digital Conversion: When human beings utter words and make sounds, it creates different sequences of vibrations. A speech-to-text model would specifically pick up these vibrations which are technically analog signals. An analog to digital converter then takes these vibrations as input to convert to a digital language.
2- Filtering: The sounds picked up and digitized by the analog to digital converter are in a form that is machine-consumable as an audio file. The converter analyses the audio file comprehensively and measures the waves in great detail. An underlying algorithm then classifies the relevant sounds and filters them to pick up those sounds that can eventually be transcribed.
3- Segmentation: Segmentation is done on the basis of phonemes, which are linguistic devices that differentiate one word from another. This unit of sound is then compared against segmented words in the input audio for matching and predicting the possible transcriptions. There are approximately 40 phonemes in the English language and similarly, there are thousands of other phonemes across all the languages.
4- Character Integration: The speech-to-text software consists of a mathematical model consisting of various permutations and combinations of words, phrases, and sentences. The phonemes pass through a network consisting of elements of the mathematical model so that the most commonly occurring elements are compared to these phonemes. The likelihood of the probable textual output is calculated at this stage for integrating the segments into coherent phrases or segments.
5- Final Transcript: The audio's most likely transcript is presented as text at the end of this process based on deep learning predictive modeling. A computer-based demand generated from the above probabilities is then produced from the built-in dictation capabilities of the device that is being used for transcription.
Source- Daffodil, Jennifer Marie