Audio Data Annotation with Unitlab AI [2025]

Data labeling is not only about images. Depending on the model and use case, it can be text, audio, or video.

Data annotation is the process of preparing AI/ML datasets by tagging or adding metadata so that models can learn patterns and generalize across new scenarios accurately. The data and labeling must be customized based on what these AI models are used for. This is quite similar to the Unix philosophy of “Do one thing, and do it well.”

In this post, we will focus on the annotation phase of a specific AI model we use daily—Audio AI.

You will learn:

• what audio labeling is
• types of audio annotation
• a hands-on audio labeling tutorial
• why audio labeling matters

Let's dive in!

What is Audio Labeling, Essentially?

Everyday voice assistants like Apple Siri or Amazon Alexa are prime examples of Audio AI in action. Any application—Google Search, for instance—that can receive commands in voice form relies on Audio AI.

These interactive systems are built on top of models that can process audio in real time, such as Mozilla’s DeepSpeech or Meta AI’s Wav2Vec 2.0.

Audio labeling is the process of preparing audio files specifically for audio-based AI/ML datasets. This could mean:

• Writing out speech as text (audio transcription)
• Identifying who is speaking (speaker identification)
• Marking sound events like alarms, sirens, or claps (audio segmentation)
• Highlighting emotions such as happiness, frustration, or neutrality (emotion detection)

Essentially, just like any other data labeling mode, audio labeling turns raw sound into structured data that AI systems can use.

How audio annotation is different

The reality is that AI systems are increasingly used everywhere in the business world across industries. Obviously, they require different types of datasets depending on their use case. Therefore, various data labeling types and techniques have evolved over time.

Essentially:

• Building face detection → image labeling
• Building video surveillance → video annotation
• Building a chatbot → text annotation
• Building a voice assistant → audio datasets

Beware that no annotation type works for every case. If you only have a hammer, every problem looks like a nail.

In a nutshell, audio annotation differs from other types in its nature and data type. The AI model you build dictates which type of labeling you do.

Types of Audio Annotation

Data annotation types share broad similarities, but differ in details. For instance, classification and sentiment analysis appear in all domains, but look very different in practice.

To illustrate different types of audio labeling, we'll go through this famous scene from Star Wars: Episode III – Revenge of the Sith (2005):

It’s a perfect example because 1) everyone knows it, and 2) it has everything we need: words, music, and raw emotion.

Audio Transcription

The most common audio labeling type. The focus here is to produce a accurate and complete text completion for video subtitles, legal materials and conference archives. This task is human-centered, for humans.

For our example, this transcription could look like this:

Obi-Wan: You were the chosen one! It was said that you would destroy the Sith, not join them! Bring balance to the Force, not leave it in darkness.

Speech Recognition

Often confused with transcription, but here the focus is machine-centered. The goal is not to produce a verbatim written record of the audio or speech, but rather to understand and act on it in real-time.

For example, if you were to upload the example audio to ChatGPT, it would act on it based on your prompt. It might have found the YouTube clip, referred to a Star Wars fandom page, or written the transcript.

Speech recognition is wider than audio transcription in its scope, and usually considered an input for the machine to do something else in the pipeline.

Audio Classification

Assign one or more labels to the entire clip. You don’t care about who spoke or when, only about the overall meaning.

For our Star Wars example, this could be a whole list of labels—"dialogue", "human_speech", "English", "high_emotion".

Audio Segmentation

It is so much easier to annotate audio when you have meaningful, bite-sized chunks to work with. Thus, this type is generally the first step in labeling audio.

This mode splits the audio into time-stamped chunks with labels. The focus is: where does one unit end and another begin? This differs it from basic audio transcription.

Our clip is not fully complete, but let's, for the sake of simplicity, assume that it was. Then, we could segment it into several chunks:

• [0.0s – 9.5s] “You were the chosen one! … darkness.” → speech
• [9.6s – 11.0s] “I hate you!” → speech
• [11.1s – 14.0s] “You were my brother, Anakin. I loved you.” → speech

Speaker Identification

Closely related to audio segmentation but different in subtle, important ways. While the goal of segmentation is to create time-based chunks, it does not automatically care who is speaking.

This type identifies who the speaker is in a given moment. It usually comes after audio segmentation. For our example, with speaker recognition added, the result could look like this:

• [0.0s – 9.5s] Obi-Wan: “You were the chosen one! … darkness.” → speaker = Obi-Wan
• [9.6s – 11.0s] Anakin: “I hate you!” → speaker = Anakin
• [11.1s – 14.0s] Obi-Wan: “You were my brother, Anakin. I loved you.” → speaker = Obi-Wan

Emotion Detection

Binary task: is there emotion present, yes or no? It does not ask when, where, what kind, whose; it just checks for the existence of any emotion in the clip.

Is there emotion in our example clip? Yes. Anger, anguish, rage, sadness—you name it.

Emotion Recognition

Emotion recognition takes two steps further and assigns emotions to bite-sized chunks generated by audio segmentation, similar to speaker identification:

In our case, this could be

• [0–9.5s] (Obi-Wan): anguish / betrayal
• [9.6–11.0s] (Anakin): rage / hatred
• [11.1–14.0s] (Obi-Wan): grief / love

Language Identification

Determines which language is being spoken. For example, if you talk to a voice assistant or a chatbot, it automatically detects your language and replies in your language. Depending on the system, it can detect dozens and identifies the correct one. This is especially useful when the AI system should work with many languages.

For our audio clip, this is easy: English.

Language Classification

Imagine a government services AI system developed for the Canadian government. In the beginning, it supports only English and French. Over time, they might add support for other predominant languages in Canada.

Initially, when the call comes to this audio AI, it might classify the request as either English or French. It might neglect or misinterpret another language, say, Ukrainian.

In essence, LangID detects a language, while Lang Classification sorts it into a fixed set of known languages.

Putting It All Together

These audio labeling types do no exist in vacuum. In practice, these types are combined to create full datasets. A typical pipeline begins with segmentation, followed by speaker and emotion labeling.

Putting it all together, we could generate this full audio point:

• [0.0s – 9.5s] Obi-Wan: “You were the chosen one! … darkness.” → speaker = Obi-Wan, emotion = anguished, language = English
• [9.6s – 11.0s] Anakin: “I hate you!” → speaker = Anakin, emotion = rage, language = English
• [11.1s – 14.0s] Obi-Wan: “You were my brother, Anakin. I loved you.” → speaker = Obi-Wan, emotion = sorrow, language = English

Pretty slick, no?

Demo Project at Unitlab AI

Project Setup

Now that you know these audio labeling modes, we will show how audio annotation is done with Unitlab AI, a fully-automated data annotation platform. First, create your free account to follow the tutorial:

In the Projects pane, click Add a Project:

Name the project, choose Audio as the data type and Audio Segmentation for the annotation type:

Upload project data. You can use the longer audio clip of the Star Wars video we have been using for illustration. Download it here:

Project configuration is now ready.

Audio Labeling

The actual audio annotation differs notably from photo, video, or text labeling. It might be intimidating for the new users, but with the help of intuitive tools and platforms such as Unitlab Annotate, it is actually quite easy.

Take a look at this video below. We first define two classes—obi-wan and anakin—and specify where they each speak. The additional configuration is there to help you annotate faster later:

The video is purposefully longer to explain the audio labeling process. In reality, it takes less time as you become more experienced with the labeling type and the tool.

If you plan to increase the quality of your audio labels, Unitlab Annotate provides additional configuration you can use to fine-tune your annotation:

1. You almost always increase audio speed to fasten the labeling process.
2. You may want to increase zoom to segment the audio clip more accurately.
3. You will probably customize your dashboard, configuring a setup that works optimally for you.

In the dashboard above, you can see that the zoom is set to 50 and the speed to 2x. I may choose to toggle off Spectogram and Timeline as I see fit for labeling the audio. With zoom, I see that my segment for the class anakin is slightly off; I will fix it, increasing accuracy.

Why Care about Audio Annotation?

So, after all these, let's answer briefly why care about audio labeling at all?

Like any AI/ML model, Audio AI needs structured audio data to learn patterns. The quality of your model depends on the quality of your labeled data.

If you are building any kind of audio AI—voice assistants, transcription services, or call center analytics—you need a well-labeled audio dataset.

Conclusion

Audio annotation is another type of data annotation. You need different data types to train different AI/ML models.

Audio labeling is more than just transcribing—it's about interpreting the audio: words, speakers, emotions, and languages so that audio AI systems can work with these audio.

The audio annotation process might look intimidating at first, but with AI-powered labeling tools and a data annotation platform like Unitlab AI, you can easily start labeling audio files.

Explore More

Check out these resources for more on data annotation:

• A Comprehensive Guide to Image Annotation Types and Their Applications
• Data annotation: Types, Methods, Use Cases.
• The Rise of Data Annotation Platforms

References

• Cem Dilmegani (Jul 3, 2025). Audio Annotation. AI Multiple: Source
• Hojiakbar Barotov (Mar 15, 2025). Who is a Data Annotator? Unitlab Blog: Source