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Audio Segmentation

Updated: Jan 4

What Does an Annotator Do?

A typical workday may include listening to audio files and identifying which parts of the audio correspond to one or more of the following: music, speech, silence, and noise (e.g., static). This process of creating segments of speech from an audio file (and typically their corresponding transcriptions) is called annotation, and annotators play an important role in the process of speech recognition technology (SRT). Here’s what you should know about this line of work.

What is audio segmentation?

Audio segmentation is a process in which human listeners can distinguish spoken words from background noise, music, and silence. By labeling what you hear, you help create a database that can be used to improve speech recognition software. Audio annotation is a necessary step in making voice-control technology more natural for consumers. If Siri isn’t always able to understand what you’re saying, it might just be because someone hasn’t labeled all of her training data yet!

What does annotation do?

Segmentation is used to distinguish spoken words from music, noise, and silence. The annotator segmenting spoken words prepares them for further analysis by other components in speech recognition systems. Speech recognition software makes use of both acoustic models and statistical language models to analyze sound segments that are identified by segmentation. Segmentation can be divided into two parts: word boundary detection and utterance boundary detection.

Why do we need annotation?

Audio segmentation is needed to distinguish spoken words from music, noise, and silence. For example, voice assistants like Siri and Alexa need to be able to understand different languages and dialects – making it a crucial component of automation.

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