“Alexa, is there a sushi place near me?”
Oftentimes, we often ask open-ended questions to our virtual assistants. Asking questions like these to fellow humans is understandable considering this is how we are used to speaking and interacting. However, asking a very casual question colloquially to a machine that hardly has any grasp of language and conversational intricacies doesn’t make any sense right?
Despite this, how do machine learning systems identify the type of questions we ask and retrieve the most accurate results from the internet? For instance, consider the same question we’ve mentioned above. The artificial intelligence system should first understand that it is not a yes or no question. Then, the device should understand that sushi is a delicacy. Next, it shouldn’t confuse sushi place with the place of sushi and fetch answers relating to Japan. Finally, it should also factor in its current location and pull good sushi restaurants nearby.
A one-line question has so many layers of detailing in it. Imagine the process and the insane amount of effort and time spent on building such incredible systems for accurate results. Thanks to NLP (Natural Language Processing), we can now conveniently interact with machines without needing any special languages. However, for this to be precise and effective, the process has to be absolutely razor-sharp.
We call it data annotation and more specifically audio annotation. Let’s get into the details of it.
What Is Audio Annotation?
Whenever you speak to a virtual assistant, a chatbot, or do a voice search, the system understands what you speak and pulls out relevant information from the internet. However, an artificial intelligence system is nothing more than an infant that is devoid of any knowledge or information during its development stages.
At that time, AI systems are fed with tons of AI training data that would train them on identifying speech elements and patterns. Volumes over volumes of audio/speech data are fed not directly into systems but passed through a process called an audio/speech annotation. This is where such audio files are added with metadata, descriptions, or additional information to make machines understand the diverse layers in an audio file.
Through audio annotation, machines are made to understand what questions, emotions, sentiments, linguistics, phonetics, intentions, and more are taught through audio labeling techniques. Every audio clip that is fed for training purposes is added with such data to classify all the diverse aspects in language so that machines can understand the intricacies involved in human conversations and either mimic them or respond to them as humans would do. The best part about NLP today is that it is inclusive in terms of accent, linguistics, and pronunciation so that regardless of how you say GIF, it would fetch the appropriate full form.
Use Cases Of Audio Annotation
Audio labeling is a crucial phase and when done right with the right datasets and proper tagging, it can be used to develop
- Virtual assistants
- Real-time translation systems
- Text-to-speech modules
- Call audit systems and more
Is Audio Annotation A Manual Task?
Yes! When we talk about artificial intelligence and automation, we only focus on results. The pre-production phase is when a team of annotators meticulously work on tagging thousands of datasets manually to make them machine-readable.
As far as audio annotation is concerned, annotators work on tagging sentence structures, grammar syntaxes, parts of speech, and more with respect to spoken audio files. When it comes to sound, they annotate vehicle sounds, their movements, traffic sounds, nature and natural sounds, ambient sounds, and even silences and distortions to make machines ignore them.
Advantages Of Audio Annotation
Though the process of audio annotation isn’t optional to assess the pros and cons, beginners need to understand that audio annotation makes your Ai-driven project successful. If your app, product, or service relies on audio sensors and NLP, it is only through audio annotation that your brand could make way for accurate results.
As a quick guide, here’s a list of advantages of audio annotation:
- It makes your machine learning modules identify different audio elements better and train autonomously without human interventions.
- Precise results can be fetched from the internet and delivered through good AI audio training data.
- Chatbots can be made to differentiate between compliments and sarcasm through audio labeling, helping you with reputation management and better-automated responses.
- Triggers can be made more precise with accurate tagging of relevant sounds or commands. For instance, the sound of breaking glasses in a surveillance system.
- Autonomous cars can be made more aware of the environment through proper audio annotation. For instance, making way for ambulances and convoys.
Audio annotation is an extremely interesting wing. With that said, it is also tedious. Since the success of your brand depends on how well your audio training files are annotated for results, it is vital that you get your AI training data labeled by experts.
It is time-consuming for your in-house teams and there are a lot of limitations with respect to data sourcing. That’s why we suggest collaborating with us for the most precise audio annotation processes. We have veterans in the field who tag every single byte of data for your training needs. So, get in touch with us today.
As Co-Founder and CEO of Shaip, Vatsal Ghiya has 20+ years of experience in healthcare software and services. Besides Shaip, he also co-founded ezDI – a one-of-a-kind cloud-based software solution company that provides a Natural Language Processing (NLP) engine and a comprehensive medical knowledge base with products such as ezCAC and ezCDI, which are computer-assisted coding and clinical documentation improvement products called. In addition, Vatsal co-founded Mediscribes, a company that provides medical transcription-based offerings in the healthcare domain.