In recent years, with the increasing market demand for AI, technological innovation and major policy benefits, artificial intelligence has become popular. According to various data and reports, the sales of smart home products are increasing day by day, and the sales volume of smart speaker products is even more amazing. There are millions of smart speakers in a brand. The application of artificial intelligence in the fields of finance and security is also becoming more and more extensive. These emerging forces have broken the traditional industry barriers and continue to develop.
There is a commonality between these fast-growing artificial intelligences, which is the need to supplement voiceprint recognition as a technology. With voiceprint recognition, it can make up for the inherent defects of these artificial intelligence; with voiceprint recognition, it can improve the performance of these artificial intelligence, make it more humanized, and thus better serve humans.
Take smart home as an example, although it can provide a lot of help and convenience for our lives through voice control. As long as we speak the instructions, we can manipulate the necessities of home life such as smart speakers, smart multimedia, and smart car systems. However, in the process of specific manipulation, people still encounter some troublesome problems.
If the sound environment is noisy or confusing, it is difficult for the speech recognition system to correctly recognize the commands issued by the user. When multiple people simultaneously issue commands or sound similar commands at the same time, it will cause confusion to the system, and it is difficult for the system to identify who should listen.
The best solution to this problem is to add voiceprint recognition technology. Through voiceprint recognition technology, vocals can be more accurately positioned and captured, and noise reduction processing is performed at the same time, so that the smart home system can accurately identify the user who issued the command.
In the field of banking and finance, the current intelligent system is mainly used for remote identity authentication. For the operation of personal financial projects such as bank lending and social security funds, security and convenience are very important. Therefore, the financial industry such as banks provides a remote identity authentication operation for the public, and can complete certification and fund collection at home. Even if the distance is far away, it will not delay things. However, the current mainstream authentication method is to verify by SMS authentication code. The security of this authentication method is extremely low. Once the mobile phone is lost or stolen, the loss of the financial account will be very serious at any time.
Therefore, for the dual needs of security and convenience, it is very necessary to use voiceprint recognition as a supplementary means of identity verification in the financial industry. Financial verification in remote situations, with sound to prevent security, greatly enhances security. The user only needs to speak the specified words to complete the verification, and the operation mode is not complicated.
The role of voiceprint recognition in the security field is even more prominent. A security system without voiceprint recognition, the higher the level of intelligence, the more dangerous it is. Imagine that when the identity of the speaker is unrestricted, it can be easily invaded by our homes, companies, and our rooms, computers, and private spaces. As long as the intruder issues instructions to the system like us, it can break through security.
Of course, with a security system for voiceprint recognition, you don't have to worry about these issues. Because each person's voice is essentially different, it is difficult to imitate or copy. Even people with ulterior motives can't succeed if they want to invade the security system by imitating our voice.
Therefore, with voiceprint recognition, intelligent systems in various industries can further improve performance and better serve users. Whether it is to accurately identify the user from a large number of vocals, or to confirm the identity of the speaker, voiceprint recognition is indispensable for these intelligent systems.
Although voiceprint recognition shows great value in all walks of life, in the actual operation stage, voiceprint recognition still faces many difficulties. The most notable ones are the difficulty of collecting voiceprints and the influence of dialects and emotionalization on voiceprint recognition.
The first step and the key premise of voiceprint recognition is to collect voiceprints. Only by collecting enough rich voiceprint information and establishing a perfect database, voiceprint recognition can be accurate and operable. Moreover, the collection of rich and diverse voiceprint information is also beneficial for deep learning of machines and systems. After all, intelligent recognition systems train and learn through a large amount of data. However, the current control of voiceprint information is firmly in the hands of the public security and government departments, which brings great difficulties to the company's voiceprint collection work. They need to do a lot of preliminary work through multiple channels, and collect enough voiceprint information to establish an appropriate voiceprint recognition system.
In addition, human dialects, accent differences, and different sound conditions in different states will have a certain impact on voiceprint recognition. If the voiceprint recognition system can only recognize standard Mandarin, the identity of a person who speaks a dialect and an accent may be difficult to identify. By the same token, when the speaker's emotions are high or low, there will be slight differences in the sound in different states. These differences will also cause errors in voiceprint recognition. Therefore, how to accurately determine the essential characteristics of sound in dynamic recognition is very important for voiceprint recognition, and it is very difficult.
Although voiceprint recognition plays an important role in various fields and applications are becoming more widespread, there are still many difficulties in specific applications. These difficulties and problems are definitely not negligible on the development path of voiceprint recognition. We need to use various technical means to adjust and solve them as soon as possible.
Beijing Junlin Technology Co., Ltd., a professional electro-acoustic enterprise, creates the golden ear of the era of artificial intelligence. Facing the world, providing the most advanced audio field solutions and professional services, based on acoustics to promote artificial intelligence technology products.
The company aims to create an audio hardware platform and system platform in the field of artificial intelligence, making Junlin technology an indispensable link in the field of artificial intelligence audio. Based on this, it covers the technical chain of acoustic technology-firmware algorithm-intelligent hardware-system driver-cloud service-big data-Ai technology.
Junlin Technology has a complete set of sound processing system solutions. Users can use existing or customized audio data collection solutions to upload to the cloud through the Junlin Technology Voice REST API, which is modeled, identified and certified by artificial intelligence algorithms, effectively creating maximum value for the enterprise.
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