How AI Speech Datasets Are Creating Impact On The Global Economy?

 


INTRO

Artificial Intelligence (AI) as well as machine-learning (ML) are now so effective and widespread that we utilize them every day without even thinking about it. One of the areas where these advanced technologies have grown with leaps and bounds — almost to the point that they can match human capabilities — is the area of automatic speech recognition technology.

 

The prevalence of technology for speech recognition is now being measured worldwide and has a growing impact on the world economy. The market for voice recognition was valued at around $11 billion in the year 2019 and is expected to grow to nearly 17% by 2025.

 

 Automated Speech Recognition (ASR) engines permit users to speak to a computer or other device which recognizes what we're saying in order to answer our request or question. This technology can be found in many applications at home and also in the fields of banking, business, marketing and healthcare.

 

Catering for diverse users

As demand for technology for speech is increasing, companies that offer software for automatic speech recognition (ASR) software are driven to design and develop new speech recognition devices that work better and can meet a more demanding range of requirements.

 

When the ASR engine is powered by powerful AI or ML capabilities It can transform spoken words from various accents and languages into comprehendible text. It can also be able detect new accents and dialects within one model for a single language.

 

The people who use speech recognition technology aren’t increasing in numbers however, they are also becoming more diverse, fragmented and diverse. This is causing one of the most difficult difficulties faced by developers of ASR engines: managing the many dialects within one language. For instance, native English users, as an instance could make use of Southern American, British, Australian or South African dialects — each with their distinct accents and different differences in grammar and vocabulary. The most effective ASRs are are particularly attuned to these differences.

 

A myriad of channels

Growing popularity of cloud computing is also driving increased utilization of channels that are not traditional. In the ideal scenario, ASR technologies should deliver seamless services on-premises and on the cloud, because the speed of moving to the cloud is typically determined by the requirements that the organization.

 

For a contact center in a bank such as a contact center for banks, customers want to be able to access self-service via voice via a range of channels which may include traditional telephony lines phones, internet-based applications and many more.

 

Dealing with these challenges the software companies that are engaged in the field of speech recognition should make use of AI and machine-learning principles and deep neural networks to be able to serve a varied user base across many different applications. This is all performed in a network structure with a reasonable speed. Businesses will be able to provide fast, seamless and user-friendly voice experiences that deliver the flexibility demanded today.

 

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Because it lets people navigate through customer interactions, seek solutions, seek assistance and access services, return products, and so on in a natural manner the use of voice technology is growing throughout every sector. Below, we will discuss only a handful of examples.

 

  • Customer analysis of intimacy
    Retail companies employ tools to study calls and conversations to better comprehend the customers they serve as people instead of grouping them into a more generic set of “personas”. A ASR driven through AI and ML is able to accurately comprehend the narrative stream and draw some of the best customer information from the conversations. Additionally when the technology is tuned to dialects and accents it can make more accurate profile of the demographics of the customer base. We are now in an age where businesses is able to go beyond exactly what people are talking about to knowing the person they are.
  • The increasing the use of virtual assistants
    Gartner estimates that in 2023 25 percent of employees’ interactions with applications will occur by voice, an increase from a mere 3 percent in. 2] Voice-enabled virtual assistants could be utilized to assist the IT help desk team, for instance in interpreting requests and carrying out routine tasks such as resetting passwords, restoring services , and other tasks. By being able to request regular assistance swiftly and easily by speaking, employees can are able to focus on their most important job.
  • Customer’s order placing
    Another use for speech recognition and transcription can be found within the consumer sector which gives customers the chance to purchase items more efficiently and quickly. Although it can take the time of a customer to browse through menus or use a succession of swipes and taps to locate what they are looking for the speech-enabled option allows customers to make a request and any additional instructions, and make an order within a few just a few seconds. This reduces frustration and increases customer satisfaction.

 

Maximizing value through capabilities of AI

AI or ML controlled ASR platforms that can combine the capabilities of speech recognition and authentication (i.e. speech biometrics) to increase the efficiency and speed in voice-related services. This kind of technology can recognize the words your customers are saying and determine and authenticate the person who is talking. In this way, the company can tell if a call is from an authentic customer or not, and without the requirement of multi-factor authentication, or screening questions that require live agents. The customer receives assistance faster agents can are able to spend less time on routine authentication and fraud is identified more quickly.

The company could want to learn more about its clients or gauge the efficiency of its agents. Instead of having to go through the transcript of the conversation and manually delineate the customer’s story from their agent’s and vice versa, the company can use the combination of voice recognition and speech biometrics to narrow down those parts that are relevant to the transcription, without the necessity of human intervention. This could save a significant amount of time, especially if the company is a large national chain that has many thousands or hundreds of hours worth of audio files.

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Conclusion

Global Technical Solutions (GTS) provides you with all the Speech Data and Data Quality Management you could possibly need to power your technology in whatever dimension of speech, language, or voice function you would want. We have the means and expertise to handle any project relating to constructing a natural language corpus, truth data collection, semantic analysis, and transcription. By giving the technological advances of this age the place of software and apps that use audio data is vital, most of these apps and software use natural language processing to function properly.

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