An embedding is a Deep Learning model’s low-dimensional representation of an input. Once an audio file is broken into utterances, those utterances get sent through a Deep Learning model that has been trained to produce “embeddings” that are highly representative of a speaker’s characteristics. In our research, we start seeing a drop off in a Speaker Diarization model’s ability to correctly assign an utterance to a speaker when utterances are less than one second. There are many ways to break up an audio/video file into a set of utterances, with one common way being to use silence and punctuation markers. This is why the first step is to break the audio file into a set of “utterances” that can, later, be assigned to a specific speaker (e.g., “Speaker A” spoke “Utterance 1”). In the same way that a single word wouldn’t be enough for a human to identify a speaker, Machine Learning models also need more data to identify speakers too. To illustrate this, let’s look at the below examples: The first step is to break the audio file into a set of “utterances.” What constitutes an utterance? Generally, utterances are at least a half second to 10 seconds of speech. The fundamental task of Speaker Diarization is to apply speaker labels (i.e., “Speaker A,” “Speaker B,” etc.) to each utterance in the transcription text of an audio/video file.Īccurate Speaker Diarization requires many steps. Learn more What is Speaker Diaraization and How Does Speaker Diarization Work? Learn why developers choose AssemblyAI's Speech-to-Text APIs.
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