How machine learning and AI are revolutionizing speech recognition technology


While it seems like something we’ve only been hearing about in the last few years, speech recognition technology was first developed over 70 years ago. It’s true though that it has come an astonishing way since thanks to developments in artificial intelligence (AI) and machine learning (ML). 

You’ll be familiar with speech recognition technology in the form of virtual assistants like Apple’s Siri, Amazon’s Alexa, and Google Assistant. Many companies including banks have also deployed speech recognition technology that the majority of us will likely have come across and used. It’s now utilized across many sectors revolutionizing the way we work, learn and communicate. 

So while the concept is not new with AI and machine learning continuously improving it means that speech recognition is an ever-evolving technology.

What exactly is machine learning and AI speech recognition technology?

AI speech recognition technology is the process of converting spoken language into text. It’s software that leverages speech recognition algorithms then translates speech from people into digital data that computers can understand and read. 

The technology essentially analyzes the audio, separates it into sections, creates a computer-readable version of it, and a speech recognition algorithm then matches it to text representation. Broadly, there are different speech recognition technologies and algorithms, with AI and machine learning being one of the more advanced of these. 

How machine learning works 

Machine learning is a subset of artificial intelligence and is a way of describing systems that have been developed to learn by itself. The technology teaches a computer to identify patterns so that it can evolve over time without human intervention.

AI and machine learning use specific methods such as deep learning and neural networks, which are often used in advanced speech recognition software. 

The algorithm is given huge amounts of data when it’s learning and it then identifies the patterns itself, not unlike the human brain. Before this technology was developed software engineers had to create code for everything they wanted to identify, but now machines have been developed to do this. 

The challenges of machine learning in speech recognition 

One of the interesting aspects of this technology is in the challenges that exist in understanding the way humans communicate. Naturally, we often use fewer words when speaking than in written form with all sorts of conversational nuances like abbreviations and jargon. 

And while we certainly use language to communicate, this goes much further than the spoken word, with some studies showing that only 38% of communication is vocal, with up to 55% relying on nonverbal cues.

There’s also the challenge that people’s ways of speaking and communicating are varied and also incorporate accents and pronunciation. There’s a distinct lack of speech standards across the globe, and context can vary profoundly across cultures. 

However, while it’s currently far from perfect, speech recognition technology has improved as machine learning’s capacity to comprehend human communication nuances has improved significantly. 

Speech recognition advantages

Despite technological challenges of the technology still developing to communicate in the ways humans do, there are currently still many advantages. Since its inception, we now have available state-of-the-art speech recognition algorithms. 

The most obvious benefit of speech recognition is that it saves time, allowing people to dictate to machines, and increasing productivity. It also can enhance user experience, reducing manual input methods like typing or clicking. 

It allows for accessibility for people with disabilities such as those with mobility or visual impairments. As well as multilingual support, efficiencies in healthcare, education and security, and many more. 

How has machine learning improved speech recognition technology?

Machine learning in speech recognition offers several additional advantages that have revolutionized speech recognition technology. These include:

  • Improved accuracy – machine learning has significantly improved the accuracy of speech recognition algorithms.
  • Adaptability – machine learning systems continuously learn and improve from new data, making them versatile and adaptable to different users and contexts.
  • Continuous improvement – machine learning models can be updated and fine-tuned easily.
  • Scalability – machine learning-based speech recognition can scale to handle a large volume of data and users.
  • Cost-effective - While developing machine learning models initially requires an investment in data collection and training, the long-term cost savings are significant, as the technology becomes more accurate and efficient over time.

There are many more advantages, and all of these benefits have made machine learning a driving force behind the transformation of speech recognition technology and its widespread adoption in various industries and applications.

What the future holds for  machine learning and AI in speech recognition

The future of machine learning and AI in speech recognition will continue to reshape how we interact with technology and each other. The integration of machine learning and AI in speech recognition technology has moved us towards a new era of human-machine interaction. 

From greater accuracy and understanding to enhanced personalization and accessibility, these advancements will have a profound impact on industries and individuals worldwide, making voice communication with machines more intuitive, versatile, and inclusive than ever before.

As the technology continues to evolve, the future of speech recognition offers even more exciting developments that can enhance our daily lives, industries, and revolutionize the way we work and communicate.