Automatic speaker identification in transcriptions

When an interview, a meeting, or any other audio is transcribed in Klang.ai, an automatic speaker identification (also called speaker diarization) is performed, which means the system distinguishes who said what. This is especially important for differentiating between the interviewer and the interviewee during interviews.

This function has significant advantages for researchers, recruiters, and journalists, where quick and accurate identification of speakers saves time and increases efficiency.

In Klang.ai's transcription view, speakers are clearly visible, and it's easy to change the speaker for a piece of text or modify a speaker's name.

Indispensable for Researchers

In research, especially qualitative research, being able to distinguish between the interviewer and interviewee is crucial. Automatic speaker identification in Klang facilitates this distinction from the start, allowing researchers to focus on the analysis and interpretation of the interview material instead of manual identification.

Efficient Recruitment with Accurate Speaker Names

For recruiters, quick and accurate speaker identification is valuable when evaluating candidate interviews. By automatically distinguishing between different speakers, recruiters get a more structured and comprehensible transcription. This not only saves time but also provides a clearer picture of the interaction, which can be decisive for recruitment decisions.

Speeding Up the Process for Journalists

In journalism, the speed of transcribing and identifying speakers is of great importance. Automatic speaker identification in Klang gives journalists the ability to quickly get an overview of the interview content. Moreover, the editing process is facilitated by correctly identified speakers automatically receiving their real names.

Editing and Adding Speakers

To change the names of the speakers, click on the name to bring up a box where you can change the speaker, rename them, and add new ones.

A user-friendly feature that enhances the user experience is the color-coding of speakers. Each speaker is assigned a unique color, enabling easy and quick identification even when the same speaker participates in different transcriptions. This visual indicator helps users distinguish between speakers and follow the dialogue smoothly.

At the heart of this functionality lies an advanced AI model trained to differentiate between voices, even when they are very similar to each other.