Speech recognition AI, especially those focused on “audio classification,” can achieve effectiveness only due to high-quality training data comprised of stacks of audio recordings. Since individuals prepare these datasets, the success of your product hinges on the data annotation service you secure, particularly when you need to “annotate audio data.” As an outsourcing provider specializing in “data annotation,” we offer a cost-effective solution by equipping your company with leading sound annotation experts, ensuring your team is built and ready in just a few weeks.
Here are the types of audio annotations your dedicated team can do:
The audio tagging professionals you outsource with us will accurately identify and label sounds on your audio. This annotation process includes creating snippets of spectrograms — wavelike visual representations of audio files. These spectrograms show signal amplitude so that annotators can distinguish voices, music, sounds made by animals and birds, noises, etc.
This task assumes data labelers match audio recordings with text files. They use verbal and sound timestamps to synchronize the voice-based streams with the text datasets. Speech labeling allows companies to teach their AI audio transcription models to identify various aspects of recorded speech: gender and age of the speaker and also the language, dialect, and accent.
Analysis of the environmental sounds on recordings, especially when trying to annotate audio recordings, is a complex endeavor. That's because extracting sounds of a metro, urban park, or shopping mall demands a depth of experience from annotators, especially when it comes to audio classification. Unlike music and speech, these environmental sounds lack consistent time patterns, making them challenging to pinpoint. Vehicles, raindrops, wind, birds, machinery, etc., serve as just a few examples of the diverse background noise that can be encountered.
For CV Artificial Intelligence image annotation of volumetric objects with volume is the next critical task. Marking 3D boxes on images is more complicated not only because data labelers have to add more points and lines. The main challenge is to estimate where to add annotations and locate them, and advanced software is the primary assistant to our tag specialists.
With this technique used for audio recognition Machine Learning models can distinguish bird singing, dog barking, and human speech, coughing, and similar events. Audio annotators have to set the start and end of each pattern from recordings made in various spaces, from different distances, and from moving and static sources. This assignment requires skills, experience, and specific tools your dedicated team will have.
Your audio annotation specialists will be able to convert voice to text with superb speech recognition tools. They can pre-process audio files in multiple languages and compression. So you don't need to alter AAC to MP3 or unify audio recording in formats before the transcription. Also, audio practitioners will ensure files meet the required specifications for the best result.
In the annotation process, while extracting specific patterns from an audio file, Machine Learning algorithms are trained to classify music genres. Skilled sound labelers, adept at annotating audio, assist in categorizing music data into genres such as pop, rock, folk, jazz, hip-hop, and more. By annotating audio files in this manner, music platforms can construct playlists tailored to listeners' preferences, ensuring they are offered songs they adore.
In recent years, intelligent home software, audio monitoring systems, robotic devices, and other tools have become more popular. So for Machine Learning audio classification of domestic sounds is critical, and many businesses invest in environmental sound categorization projects. However, these recordings often contain noises and unrelated or different sounds together, so only top-notch labelers can process them.
Training datasets with marked lines, polylines, and splines is the heart of autonomous driving. Helping machines see road markings and signs is quite a simple but responsible task. And we can find labelers with extensive expertise in annotating bus stops, crosswalks, pavements, bike lanes, double and dotted lines, and other traffic elements.
Some sound samples can possess more than one label, and these labels can appear several times in one recording. For example, one song can evoke different emotions, and professional labelers have to tag them throughout the entire audio file clip. The ML models will then choose the songs that best fit the mood listeners prefer.
Which industries can leverage professional text marking and structuring? Here are just a few cases:
Many online and brick-and-mortar shops optimize their content and descriptions for local voice searches. And this sales channel brings them more revenue every year.
The future belongs to home voice-smart gadgets like TVs, virtual assistants, microwaves, door locks, thermostats, lighting, trash bins, window blinds, fridges, air conditioning controllers, etc.
Speech recognition hands-free devices help drivers find the way, get information, or communicate without stopping. This allows drivers to follow the safety regulations and secure themselves and their passengers.
The growing number of transactions makes banks search for new and more secure ways of client identification. So they start implementing voice authentication programs into their customer support tech stacks.
Voice automatic speech recognition and tools let doctors dictate clinical statements, convert them into text, save them, and share them with colleagues. This way, they spend less time with each patient and note all critical details.
Voice-controlled wearables entered our lives years ago. And they don't seem to stop. This happens because voice technology evolves, letting us enjoy small-screen or screenless devices and incorporate them into our routines.
With voice-powered software, customer service agents can handle more complicated queries. While voice assistants and conversational interfaces answer straightforward questions, business owners can close more requests with fewer specialists on board.
With so many opportunities to travel and communicate, real-time voice translators have become invaluable. And modern speech recognition tools allow us to understand almost 100% of what the other person says.
You probably have checked tens of audio annotation companies but still haven’t looked at the benefits of working with us:
Being a Kyiv-based company, we offer data and audio annotation tools and machine learning services from Ukrainian data labelers. This option will let you cut your team's salary and administrative costs and spend around 20% less. Payrolls of audio annotators from Ukraine are significantly lower than what their US and European colleagues earn — and that's due to the lower cost of living.
Companies love the format we offer because they don't have to handle tasks not relevant to work. We take on everything from creating job descriptions to onboarding newcomers. And while we buy the equipment and arrange salary repayments, you keep focusing on business.
The issue with managing remote teams is different time zones. And some outsourcing destinations will leave you with only 2-3 shared working hours with your own audio annotation project team. But Ukrainian data labelers will be available during business hours for companies in the USA and Western Europe.
We hire audio data practitioners from Ukraine — one of the largest and most promising IT hubs. So our recruiters will contract specialists with years of the audio labeling experience in your industry. We also consider your corporate culture and try to get the best-matching people on your board.
Our recruiting managers make lists of top annotation experts ready to start working ASAP. They create talent pools and regularly update them. So once a client needs those professionals, they can join your team shortly. This allows you to launch your project soon.
Get in Touch to Equip Your Project with Top Audio Annotation Service!
Check our cooperation models to optimize costs for your speech and language annotator or a dedicated team.
This option will suit companies with large ongoing ML projects that regularly need training datasets. We build a team of 2, 10, 15, or more audio labelers dedicated to your projects. You get complete control over these specialists and are free to schedule their tasks as you need.
If your company only starts its journey in AI or needs to get the first audio dataset for Machine Learning models, select this cooperation format. You’ll pay for what you get, hiring a sound labeler for several hours a day. This approach is flexible and cost-effective, which is critical for startups.
Are you sure you’ll have enough work for one sound annotation practitioner, but it’s too early to hire several specialists? Then this model will be a perfect match! Pay a fixed monthly fee and prioritize tasks to your dedicated audio labeler. We’ll take care of all organizational issues while you concentrate on training datasets.
