Speaker Identification with Perfect Harmony of Algorithms in Artificial Intelligence
Artificial intelligence robots or synthetic assistants that we imagine existing in the future will not appear out of nowhere. Humanoid artificial intelligence will be built piece by piece, just like a little charmed particle of a LEGO, and they will become masterpieces of a series of technological revolutions slowly being put together. Therefore, every development of machine learning or deep learning, which are baby steps, constitute the developmental milestones of this revolution.
Artificial intelligence components consist of algorithms that recognize, describe, and act accordingly. We are in a process where its development will never stop and every revolution will give rise to a new formation.
Evolution of Speaker Identification Technologies
By using the developed technologies, it is possible to identify a person’s hand, fingerprint, sound, eye, signature, retina, ear shape, DNA, keyboard basis pattern, smell, and gait criteria in every environment. There are unique human measurable physiological indicators that are used for automatic identification or verification. These tangible human characteristics can’t be forgotten or lost unless there is physical distress or attack. Because they are always with us.
The value of auditory information is increasing day by day. We know it is there, but not visible, however, it can be heard. For example, NASA strives to detect sounds (signal) coming from the universe due to figure out the answers about the big bang or secrets of space. In that case, what developments do sound identification technologies, which are remarkable even outside the world, an exhibit on the earth?
Speech recognition technologies are the most important and indispensable branch of the evolutionary tree in artificial intelligence. Usage fields of speech recognition technologies increase widespread due to easy development and practicability. The best known of these applications is Apple’s Siri, Amazon’s Alexa, Google’s Google Assistant, and Microsoft’s Cortana. These are based on basis of NLP algorithms. Studies in the field of NLP date back to the 1950s. In this sense, like the algorithms and sound processing techniques developed over time, they started to work in an integrated manner. With the strengthening of computers, the use of these techniques has continued to be widespread and developed.
In the traditional method, speech recognition progressed more by converting speech to text, analyzing it, then converting it back to speech. On the other side, direct speaker identification systems have been developed with new current algorithms. Thus, architectures that focus directly on the sound without any textual transformation processes were built.
By using deep learning methods, new possibilities encountered by the system in different issues can be reprocessed. In this way, content-independent systems can be produced. Considering the development of speaker identification systems, it is aimed that all the steps mentioned will work independently from subject-based variables using deep learning methods. Additionally, a system to be prepared in this direction must have high software and hardware (such as GPU) processing capabilities.
Image and sound are unstructured data. However, the image has more features that can be processed than the sound. So, deep learning algorithms are widely used for image processing or computer vision. However, it has recently been realized that processing the sound with deep learning is more effective than the traditional methods.
EVAM Speaker Identification
EVAM successfully realized the project that goes one step ahead of time on the speaker identification module developed with deep learning. Also, EVAM gathered different artificial intelligence algorithms in deep learning and brought systems such as speech/speaker recognition, identification, and verification together under one roof. And the exciting part is, the module works with high accuracy.
The module, which can successfully work on all languages, has primarily trained on data of Turkish speakers. As is known, the English language is preferred in the development of such algorithms since there are lots of data. However, although there are few data sources for the Turkish module, it has been revealed in the test results that it has a high accuracy rate. The result shows us that the module can perform high accuracy for other languages as well.
What are the Areas of Usage in Real Life Problems?
EVAM speaker identification module can be used in all EVAM’s products which allow end-to-end customer journey orchestration. EVAM Actions enables enterprises to design customer journeys in real-time or manage customers’ engagement, demand, and expectations from banking to e-commerce. EVAM Intelligence, which is a continuous intelligence tool, gives a chance to understand what customers may need as the next step. EVAM Rule of Things (RoT) allows you to design device journeys to manage big data and use the internet of things on a platform that is secure, fast, and useful.
EVAM allows this speaker identification to be used for voice signature as a service in mobile security stages. Also, it can be used in many different sectors such as banking, insurance, e-commerce, telecommunication, etc.
With the speaker identification module, companies can obtain much knowledge from customers speaking. For example, it can deduce information such as where the customer lives, psychological condition, age, or gender from the accent and frequency of the speech. Moreover, it can instantly predict the problems that may occur in the call center customer experience, based on the speech of the customers. Also, speaker diarization (overlapped speaker detection) of more than one speaker and identifying the target speaker.
EVAM speaker module also can detect malfunctions that can be machine or engine troubles with EVAM Rule of Things from the incoming sound data in the production area. Besides, it can contribute to getting sharper results by contributing to IoT data used in predictive maintenance estimation.
How Does It Work?
Let’s try to explain the working logic of the module in a simple way. First of all, the unstructured sound data are extracted and transformed into structural data. In the process of structuring the data, the roughness that will affect the model such as noise and sound pollution is cleaned. It is then determined whether there is a speaking situation. At this stage, it is checked whether there is more than one speaker or if the speaker has changed. If the speaker is more than one and overlapped, separation is done. The clustering algorithm, also known as the machine learning technique, is used. Conversations re-segmented as a result of these transactions are classified and tagged to their owners.
The part described so far forms one side of the module. On the other hand, with the structured sound data, who this sound belongs to or not is recognized by the deep learning algorithm. If the identified data has left any information previously, it is used to match aiming to verify whose sound it is. Moreover, all these stages take place in real-time, which expands the usage of this module in various industries.
Conclusion
The speaker identification module shows that big data is transformed into a truly meaningful form, can be used for many different fields of industry. There lies a treasure in the unstructured sound data. Built to reveal this treasure, the use of this module is definitely designed to push the limits of the imagination.
