We come from situations like the following: you have just finished your interview and want to record some chunks of the audio or the video because the content is relevant; anyhow when you try to turn the speech to text the audio is of low quality and hard to understand. The outcome is discouraging. You may have relied on a supposedly excellent recording, and the result is always the same. That uncertainty is inadmissible!
AI, DL and ML have evolved, and the speech-to-text of the past has become a reliable tool from the point of view of productivity, time-saving and above all accuracy. The software is optimized to the point of detecting a variety of voices and services, to assist users, from whichever activity and field, and make their work and life easier, faster and perfectible. That minimum error rate (0.01%) will inevitably happen when you transcribe speech content.
What have Zyla Labs developers gone through to achieve accuracy in an automated transcribed text? They had to overcome the inconveniences of overtalk (people speaking simultaneously), background noise, accents, coded language, numbers, etc. What have they accomplished? A suite of APIs that get that excellent transcript, with the functions to minimize mistakes to 0.01%. Accuracy of transcription grows along with the development of AI, and the North is to achieve 100% accuracy.
English Speech-to-Text API guides you to produce the perfect scenario: appropriate environment, perfect transcription, automated process, so as to get flawless text. The API generates a strong algorithm and you are requested to go through the checklist to confirm the expected quality; thus the outcome will be as accurate as possible.
The biggest inconvenience that can affect the quality of the transcript is the effect of background noise. Consider that environmental conditions go beyond your reach. The selection of the apt location is crucial, and public places can be a drawback. Zyla has a great algorithm that will warn you whether the surroundings will hamper the quality of your recording. It admits environmental factors that are not too invasive. The position of the microphone is also crucial, proximity to the speaker and minimum interference are the key.
Never forget that the speakers` words are the most precious element in the situation. Even if the environment is noisy, the software will help you overcome this to optimize your recording, guaranteeing the best possible accuracy of a transcription without flaws or interruptions.
There is another strategy to make sure your audio will be crystal clear, and that is the prevention of overtalking. It sometimes happens when speakers overlap interventions. The best recommendation is the quality of your speech: short, sharp questions. If you have more than one speaker, allow individual intervention, and eventually use a follow-up question if overlapping happens anyhow. Bear in mind that the API has a timestamped service that selects speakers and gives accuracy to your recording.
English Speech-to-Text API admits accents and dialects, as well as specific jargons and coded language (numbers, dates, graphs). This is a feature that is unbeatable in the attempt to accomplish accuracy. The software is constantly being optimized to render an excellent solution. It is complemented by a suite of APIs (Get Transcription Result API, Transcribe audio into Text API, Transcribe Speech API, etc.). A bright future shows ahead!
Remember that the API will grow in accuracy with practice. The more you use it the more accurate it will be. Zyla offers reliability of accuracy as well as time saving and functionality.