Blog: The Benefits- Here’s why you need deep neural networks-based speech technology in your software solution

Over the past decade, speech recognition technology has progressed and improved significantly. Since 2010, speech recognition has become commonplace and a part of our daily lives. With the introduction of voice assistants like Siri and Alexa, for example, but also for help to perform voice search queries and send text messages. A recent statistic on usage quoted by Serp Watch (https://serpwatch.io/blog/voice-search-statistics/), suggests that there are around 4.2 billion digital voice assistants worldwide.

Not only has adoption increased drastically, but with the introduction of neural networks and deep learning, the fundamentals of speech technologies have evolved considerably. Meaning more intelligent, accurate and reliable technology making substantial differences to reporting quality and efficiency.

Prior to the introduction of DNNs, previous machine learning based methods utilised algorithms and probabilities alone. Today’s technology is far more comprehensive and all-encompassing, utilising Deep Neural Networks (DNNs) which are particularly effective at identifying similarities.

Powered by deep learning and neural networks, today’s speech recognition technologies have made remarkable advancements in accurately transcribing speech into written text. In this blog, we will explore some of the benefits of using speech recognition based on deep learning and neural networks technology and delve into the difference this AI-powered technology makes to end users.

Some of the key benefits of utilising a deep learning and neural network-based speech recognition SDK in your solution include:

Higher Accuracy: Deep learning techniques, particularly deep neural networks (DNNs), have significantly improved the accuracy of speech recognition outputs. Even with the most complex terminology and the strongest of accents. DNNs can capture complex patterns and features in audio data, resulting in more precise transcription. There is no longer a requirement for voice profiles to be trained to the individual user. Each time a person speaks there will always be differences with pitch, tone, volume etc depending on varying factors including things like the environment, whether the individual has a cold or whether you’re dictating fresh in a morning or tired late afternoon.

With the introduction of DNNs, learning happens within the first few seconds of a dictation and continues throughout the duration. The key differentiator with DNNs is that the output is the total sum of everything – it is not an output based on limited probabilities alone. For example, in instances where there may be fewer common pronunciations or variations, the network will work through thousands of possibilities to arrive at the most logical output (considering every input detail available). There is no longer a need for the training of voice profiles as the understanding within deep learning and DNNs is far superior. Accuracy levels are of very, very high standards.

Robustness to Environments and Variations in Speech: Deep learning models can handle variations in speech, such as accents, dialects, speech impediments, and background noise, more effectively than traditional systems. With deep learning and DNNs, conclusions are not based on probability alone. Neural networks combine the knowledge and understanding of all data provided when considering the output. An example being the English ‘th’ sound. In 10 million cases, this word would be recognised as ‘the’ but this probability doesn’t account for non-native English speakers with accents where this sound may be interpreted as an ‘f’. Modelling accents is very difficult when working on probabilities and averages. Neural networks utilise all information available to consider every scenario to arrive at an output conclusion.

Similarly, when considering background noises when dictating. No two recordings will ever have exactly the same background noise. There will always be some variations – be that a fan in a busy pathology lab, background conversations, phone calls, animal noises, traffic and more. With the introduction of DNNs, recognition is far greater and robust to background noise.

Continuous Learning: It doesn’t just end here. Deep learning models can be continually trained on wider datasets, allowing them to continue to learn. For example, to adapt to changing speech patterns, languages, and the introduction of new technologies.

The benefits that the introduction of DNNs in speech technology bring are significant. The technology evolution benefits clinicians, lawyers, and business professionals across the globe, enabling increased productivity, improved quality and efficiency gains.

Interested in learning more about how our AI-powered, speech recognition SDK can benefit your software solution? Get in touch