Artificial intelligence (AI) has a significant impact on both human life and numerous sectors. Its technologies, such as deep learning, and machine learning, are becoming increasingly capable of teaching businesses how to complete complex tasks. The ideal example of this would be self-driving cars that are on their way to the streets. Some diseases are also being treated with AI technology.
Despite these significant advancements, one basic capability remains elusive: language. Siri, Amazon’s Alexa, and IBM’s Watson can interpret and follow simple voice or text-based commands, but they can’t hold a conversation and don’t have an actual grasp of the language they employ. This must change if artificial intelligence is to be genuinely transformed.
Importance Of ASR Technology
Accents and dialects create an additional barrier to communication. Speech technology must overcome the language barrier in order to provide understanding, context, and value to a discussion. The voice needs to be easily understood and acted upon when it comes to automatic speech recognition (ASR) technology.
Speech recognition businesses like Speechmatics and Paperspace are looking into ways to deal with the problem of accents and dialects.
Making a speech recognition engine that is optimized for accent-specific language models could be a viable approach. An ideal example would be establishing a language pack for Mexican-Spanish, Spanish-Spanish, and other languages. This method produces excellent accuracy for a single accent, and it produces highly accurate academic findings in the vast majority of cases. ASR method necessitates the use of the appropriate model for the appropriate speech, and there are times when this solution is ineffective.
Another option is to create a voice recognition engine that can recognize any Spanish accent, regardless of place, accent, or dialect. The technical ability to build an engine in this manner, as well as the time it takes to build, are both issues with this strategy. The frictionless and seamless user and customer experiences, on the other hand, speak for themselves. Only the accent-specific option was considered by ASR engineers as a credible way to close the accent gap. From a technical standpoint, limiting the challenge to a single-accent model made sense because it was the most efficient approach to achieve the best accuracy for the specific accent or dialect.
ASR providers were also expected to develop specialized models for various markets as part of this endeavour. A medical organization, for example, would require a whole different language than a utility company, posing a significant problem for ASR technology. Back in the late 1990s, instead of being speaker-independent, engines required the user to train the ASR technology for their speech.
AI-Based Voice And Translation Services
In fields like government, healthcare, education, agriculture, retail, e-commerce, and financial services, AI-based speech recognition and language translation technologies can have a far-reaching positive influence. Text-to-speech services turn text into a human-sounding synthesized voice that can be tailored to the service or brand. There are now technologies that allow users to enter text in Indian languages for internet searches or translations, as well as have various email addresses in multiple languages.
Artificial intelligence (AI) speech and translation models are rapidly being used by businesses to automate contact centre queries, create intelligent voice assistants, and allow voice interfaces for smart devices and apps. Others are using neural text-to-speech systems to provide users with natural-sounding speech while interacting with voice assistants. Language services can assist businesses in gaining insight into what their customers think about their products and services, allowing them to take actions to improve trust and engagement. Sentiment analysis, opinion mining, and keyword extraction are examples of such services. As a result, Artificial intelligence (AI) solutions are influencing how customers interact with businesses and brands.
Conclusion
ASR technology providers have been able to expand the bounds of what is feasible with voice technology as computation and machine learning have developed and grown over the last decade. Engineers realized as the technology became more extensively utilized that they would never know the speaker’s accent or dialect before using the technology, simply the language.
An all-encompassing language model may not provide the best accuracy for a given speaker, but it is likely to provide the best accuracy for that language overall. Speechmatics and Paperspace are planning to develop an any-context recognition engine that will allow them to create accent-agnostic language models. They discovered a means to create language models with a tiny enough footprint that their ASR technology can be used in the real world.
So, if you wish to know more about ASR technology in business, contact the ONPASSIVE team.
