Audio annotation purpose and techniques

Many smart applications ranging from chatbots and voice assistants to security systems with speech recognition capabilities are the products of the up-and-coming field of artificial intelligence – machine learning.

These advanced features of smartphones and computers, in turn, are only possible thanks to audio annotation. We will look at the ways of audio annotation and why it is essential for the modern technologies needed below.

Audio labeling

Audio annotation or speech labeling is the procedure of giving labels and metadata to audio recordings and transforming them into formats that can be comprehended by a machine learning model.

Audio labeling represents a vital technique for designing robust natural language processing (NLP) models. NLP refers to a machine learning method that enables machines to interpret, manipulate, and comprehend human language. The NLP market represents a great area of interest since the AI model with such capabilities is in high demand by companies.

Although it is necessary to note that audio annotations are not only useful for classifying audio components coming from people but also different sounds from animals, background noise, the environment, instruments or vehicles, etc.

Annotating audio, like all other types of annotations, require both manual work and special software for the annotation process. In the case of audio annotations, experts point out labels or tags in a given recording through the use of applications and feed the relevant audio information into ML models to create a trained system.

To properly annotate audio, experts often have to first transcribe it into text form or break it up into sections. Frequently it also happens that an entire audio file is assigned a label or metadata.

Audio data

Audio annotation is not the only one kind of annotation. Labels are also applied to, for example, video, images, or text. Clearly, the purposes of these types of labeling will be different, as well as the data to be labeled. In the case of audio annotation, the object of annotation is an audio file.

The audio files dataset for annotation has to be large so that the future system has as much context as possible to solve the tasks it faces. Apart from the amount of information, you should try to create a high-quality annotation with the correct labels.

Even If you have a small dataset, you should try to develop a workflow that allows you to perfect the dataset. For quality ML models, the quality of data collection is as important as its quantity.

Audio annotation types

Without audio annotation, many tasks would not be possible. There are different types of annotation for each specific purpose and they are as follows:

Audio classification

The annotators classify each audio recording into pre-specified classes to perform classification tasks. Such categories may include connotation, number or type of speakers, their language or dialect, background noise, intentions, or semantics-related information.

Music classification by genre or instrument also assumes sound annotation. Recommendations of tracks based on what you have listened to, as well as the organization of music libraries, are possible due to it.

Audio transcription

Annotators convert the audio file into text, which is then annotated. Audio files may be of varied quality and contain interfering factors, such as background noise or features of pronunciation, all of which have labels assigned to them. Transcription converts sound into text which is critical for training ML models for making sense of human speech.

Multilingual audio file collection

To generate datasets of annotated data a crowd records possible user requests to voice assistants. As an alternative to spoken words, it can be various sounds, such as sneezing or humming a tune. This kind of data makes it possible to create smart systems, such as the already mentioned voice assistants, which can make our lives better and easier.

Side-by-side audio comparison

Such comparisons involve annotators listening to two or more audio files to determine which one best fits particular criteria. For instance, annotators may use context to identify the recorded speech that sounds most natural, or whether the voices of several speakers match.

How audio annotations are used

Once the audio annotation is completed and training data is collected, specialists may proceed to the creation of ML models that will have the ability to perform the following functions:

Voice assistants

Voice assistants respond to the voice command of a user. Such systems are also trained based on labels. Virtual assistants can recognize and synthesize speech, report the weather forecast, or make a query in a search engine. These virtual assistants help people who cannot type. For example, elderly or disabled users.

Speech emotion recognition

Emotional content detection of the audio allows to identify the feelings of the speaker: joy, sadness, rage, anger, fear, astonishment, and so on. This allows to automate the process of monitoring the quality of customer service in call centers.

Natural utterance collection

Natural language utterance annotation requires specialists in data annotation to classify minute details of a speech. They create labels that describe the extracted natural language utterance in terms of intonation, dialects, semantics, context, correct punctuation, and intonation.

Automatic speech recognition

Also referred to as Speech-to-Text. In this case, speech-to-text transcription is required when annotating. An entire audio file can be segmented into smaller fragments, each with its own features on the audio recording track. Experts teach the ML model to match these audio features to text, and then learn how to reproduce text from these examples independently.

Text-to-speech

Also known as speech synthesis, this technology works similarly to speech-to-text, but in different order. Specialists annotate audio recordings with textual content and teach the ML model to match text to audio. Then the smart system can reproduce voice from this data without any external help.

Speaker diarisation

It is the process of adding marked areas to audio streams and determining the start and end time stamps of speech allocated to different speakers.

What is the best way to do audio annotation?

There are various ways to carry out audio annotation. The most common ones are:

• In-house annotation. Data annotation by an in-house team of experts offers many benefits. They are most likely to ensure high accuracy and quality of the work. The downside of this approach is that it is frequently one of the most high-cost ways, and requires employing a large number of professionals.

• Crowdsourcing. Crowdsourcing platforms as opposed to in-house annotation, is a more cost-effective method. They allow a large number of people from different parts of the world to join in and perform the task of annotation.

• Outsourcing. Outsourcing may consist in hiring freelancers or a specialized company that offers experts to fulfill the task of annotating.

Conclusion

Data annotation is an integral part of any machine learning system. Professionals create sound recognition models that allow developing chatbots, machine transcription, translation software, language learning, pronunciation assessment software, and speech recognition systems.

For the resulting machine learning model to meet all QA requirements, there has to be plenty of qualitative data with appropriate labels assigned to it. The critical factor for collecting such a dataset lies in the fact that the responsible managers have to choose the right methods and approaches for annotation, at the beginning of the project.