What is Speaker Diarization?

“The computer takes in the waveform of your speech. Then it breaks that up into words, which it does by looking at the micro pauses you take in between words as you talk.” 

– Meredith Broussard

 

If we talk about the earlier times then speaker diarization itself was not a stand alone technique. In fact it was mere algorithms that were used for speech recognition to enhance the adaptive processing and the development. What changed then? How did these algorithms become so powerful that Speaker Diarization in itself became a technique?

 

The answer lies in the development of technology, especially deep learning. With deep learning a new revolution began and bought a lot of changes in Speaker Diarization. Speech recognition in itself is a big problem that has been solved through technology. Diarization has bought more ease and understanding to it. 

 

In this blog you will learn about Speaker Diarization and understand the different components involved in the process.

 

 

What is Speaker Diarization?

 

It is a simple process wherein the audio is divided into multiple small segments based on the individual speaker in order to identify who says what. Now to identify the speaker and his behavior we need to use AI and deep learning. It is also very helpful to make different speech analytics apps. 

 

Marketers and businesses can face a lot of issues when they try to understand human conversations but with the help of Speaker Diarization this process becomes very easy. In day-to-day conversations we cannot extract everything. Therefore, it is critical to extract the relevant information from the cluster of data.

 

For example - If you and your friends attend an important meeting then you can prepare valuable notes with the help of speaker diarization. In case you are regularly attending meetings then with the help of AI new files will be created to store the deciphered information.

 

How is recording conversations useful for you? When you are speaking on a topic on which you have already spoken about, it is important to match your previous views. If you express hatred on a topic on which you expressed love earlier, it can lead to controversies. Therefore, it is important to match the previous views or remember what you have spoken in the past.

 

There are 4 different types of conversations that you can break with the help of Speaker Diarization. They are: 

 

1. Customer Conversations

2. Social Talks

3. Support Calls

4. Sales and executive conversations

 

“When we compare automatic recognition to human performance it’s extremely important to take both these things into account: the performance of the recognizer and the way human performance on the same speech is estimated.”

– Julia Hirschberg

 

The conversations are broken into smaller segments and analyzed with past information. This way marketers can also enhance the overall customer experience.

 

Also Read | Customer Experience Trends

 

Components of Speaker Diarization

 

We already read above that in speaker diarization, algorithms play a key role. In order to carry the process effectively proper algorithms need to be developed for 2 different processes.

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Processes in Speaker Diarization

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1. Speaker Segmentation

 

Also called as Speaker Recognition. In this process AI algorithms analyze the features of voices and the zero-crossing rate. With the help of speaker segmentation we can identify the gender of the speaker- male or female by analyzing the pitch of the voices.

 

2. Speaker Clustering

 

Once the gender of the speaker is recognised, the next step is clustering. The entire conversation is labeled and divided into clusters. In order to identify the number of speakers in the conversation two approaches are used. One is a probabilistic approach and the other one is deterministic.

 

• Probabilistic Approach

 

In this either GMM or HMM is used to decipher different features and patterns, vowels and syllables from the conversations. GMM stands for Gaussian mixture models. HMM stands for Hidden Markov Models.

 

• Deterministic Approach

 

Based on a single metric the entire speech is divided into similar groups or clusters. The metric is decided either by the analysts or the businesses performing this technique.

 

Also Read | Text Generation using Markov Chain

 

 

Supervised and Unsupervised Speaker Diarization

 

The machines that are used for Speaker Diarization can be trained in two different ways namely- Supervised and Unsupervised. 

 

The rate of error is the most important thing to look for in the final result. Both these methods have a different error rate. In Diarization error rate is defined as the fraction of time ( seconds, minutes) that are incorrectly attributed to the speaker.

 

1. Supervised method

 

This approach will give you less errors in the final transcript. The result and the decision framework will be based on a formula instead of saving the whole speech in the machine. But the main drawback is that it can only be used for offline recordings. 

 

There are other drawbacks of this method as well. It requires more manual labor and help. Since the machines are trained by humans, any error on our part means we have trained the machine in the wrong fashion as well.

 

2. Unsupervised method

 

In this the machine is not trained rather it is left loose. It can tap into any unlabeled conversation or try to find any trend/ pattern in the conversations on its own. 

 

Since a machine carries out everything on its own, the chances of errors and mistakes in the final result also increases. But unlike supervised methods the role of humans is very less and a lot of manual labor is saved.

 

Also Read | Self Supervised Learning

 

It is worth saying that the Speaker Diarization technique is playing a major role in different tasks of navigation, retrieval, and analyzing large amounts of data. The error rate can be reduced with it and the results always show robustness.