Sound Labeling involves the separation of all the needed sounds and labeling them. The speech containing different types of sentences and words are annotated while relating them with the spoken words and their meaning. For example, these can be certain keywords or the sound of a specific musical instrument, event tracking, etc.
The main purpose of audio classification is listening and classifying the audio recordings. Using this data, the machines are able to differentiate between sounds and voice commands and identify the number or type of speakers. This type of audio annotation is important in the development of virtual assistants, automatic speech recognition, and text-to-speech systems.
Sentiment annotation involves the process of determining if a segment of speech is perceived as positive, negative or neutral. Audio sentiment analysis is more accurate than text sentiment analysis because audio provides additional information such as the emotional state of the speakers. It helps to identify customer’s opinions, customer experience, and needs, social media trends, etc..
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