Speaker Identification on Multi-Party Calls: Distinguishing Callers When Three People Are Talking

Speaker Identification on Multi-Party Calls: Distinguishing Callers When Three People Are Talking

A transcript gives a detailed report regarding the actual telephonic conversation. However, what if the conversation involves three people?

A customer explains an issue. A spouse adds more details. An agent asks follow-up questions. Multiple people speak at nearly the same time.

If the transcript simply records everything as one stream of text, you are left with a bigger question. Who actually said what? This is where speaker identification calls and speaker diarization calls become important.

What is Speaker Identification on a Call?

Identifying a speaker signifies the process of realizing which of the participants was speaking at a given moment of the call.

Suppose three participants are engaged in a phone conversation:

Without speaker identification, the transcript may be produced in a single line that you can understand the conversation, but you don’t identify who said what? That’s why, a speaker-aware transcript organizes the conversation as;

This distinction becomes particularly valuable when more than two people participate in a call. Instead of simply transcribing every word, speaker identification helps businesses separate participants and understand who provided each piece of information.

What is Speaker Diarization in Calls?

Speaker diarization calls refers to the technology that helps to understand the “who spoke when” aspect of an audio conversation. It uses the difference between the times when one speaker finishes talking and another speaker starts to divide the speech into parts of conversation as:

This distinction becomes especially important in multi party call transcription where several people join the same conversation. Instead of receiving one continuous block of text, businesses can review a structured transcript that is easier for humans and AI systems to understand and analyze.

Why Does “Who Said What” Matter?

Understanding what is said on a call is useful. But knowing who said it can make that information even more valuable.

The customer provides information about the incident, the spouse gives another piece of information, and the agent asks a question to complete the claim. When the transcript includes all these voices in one chunk of text, it is impossible to understand which speaker contributed to what part.

However, a speaker aware transcription can separate the conversation:

Now the conversation is much easier to understand, helping the team make a difference in several areas:

Better Call Quality Reviews

With the help of transcripts, it will be easier for supervisors to assess the way agents perform and how the clients are responding, as well as to locate the significant turning points in the conversation. This allows for the identification of the missing questions, when escalation happened, and what should be improved during training.

More Accurate Customer Intent

Not every statement during a call comes from the customer. An interpreter, caregiver, spouse, colleague, and another participant may ask questions, make suggestions, and provide additional information. The transcript that does not indicate different speakers will make it impossible to determine who gave a certain piece of information or if it is really the response of the customer. That’s why a multi-party call transcription is important for accurately capturing the conversation.

More Useful Call Summaries

A basic transcript may capture the statement, but a speaker-aware system can understand that the customer initially provided one date; the spouse corrected it, and the customer confirmed the corrected information. This allows AI to create a more useful summary, such as:

It allows both agents and supervisors to understand important information without reading the whole conversation.

How Does Multi-Party Call Transcription Work?

A typical speaker-aware transcription workflow can be thought of in several stages.

1. Capture the Conversation
   The voice system receives the audio from the customer conversation that may involve multiple participants.
2. Detect Different Speakers
   AI distinguishes one voice from another and creates separate conversational segments by analyzing characteristics within the audio.
3. Transcribe Each Segment
   The transcript maintains speaker boundaries and converts speech into text from each segment.
4. Assign Speaker Labels
   The system associates sections of the transcript with speaker labels that can be mapped to known roles or participants.
5. Generate Context of the Conversation
   Dividing the dialogue in various speaker portions makes it easier for the supervisors to access the context in which the people taking part in the conversation, like who asked what questions, who cleared what doubts, who provided the information, and so forth.

Conclusion