Speech Recognition Using Artificial Intelligence

  • Posted by AISmartz
  • /
  • October 14, 2019

According to a Capgemini study in 2019, 74% of the digital services users depend on their voice-based assistant to research and buy products and services, create a shopping list, and check order status.

Now, we’re all familiar with Siri, Alexa, Echo, and Google Assistant. However, have you ever wondered how these digital assistants understand your questions, and how do they determine what exactly do you require?

Well, your voice assistants use the speech recognition technology to determine what you’re asking them to do.

But, What is Speech Recognition?

Speech recognition is the process that enables a computer to recognize and respond to spoken words and then converting them in a format that the machine understands. The machine may then convert it into another form of data depending on the end-goal.

For example, Google Dictate and other transcription programs use speech recognition to convert your spoken words into text while digital assistants like Siri and Alexa respond in text format or voice.

An advanced form of speech recognition also entails voice recognition, that is recognizing a person or the source based on voice/sound.

Why do we Need Speech Recognition Capabilities?

According to a study conducted by Research and Markets, the global speech recognition applications market would be worth USD 18 billion by 2023, growing at a CAGR of 23.89%.

Speech recognition is widely used in digital assistants, smart speakers, smart homes, and automation for a variety of services, products, and solutions.

From your smart lights that turn on or off on your command, Google Home Assistant that can place space trivia with you and make financial transactions when requested, Alexa that can place your grocery order and call you a cab on your behalf, to automobiles, refrigerators, and washing machines that follow your voice commands; speech recognition is the component of the system that makes it all possible.

Speech Recognition and AI

In traditional speech recognition frameworks, many practical complexities need to be dealt with in the case of traditional speech recognition systems. First of all, natural language has various components like accent, semantics, context, and words from foreign languages. Further, the traditional algorithms used to perform speech recognition have limited capabilities and can identify a limited number of words only. These algorithms are not capable of adapting as languages change over time. Finally, the accuracy rate of traditional algorithms is poor, making the speech recognition system unreliable.

With the advent of AI and machine learning (ML) models, the capability of algorithms improved exponentially. ML models can process a much larger dataset with more accuracy as compared to traditional models. Further, the ML models can improve their accuracy and adapt to changes in language on their own, thanks to their self-learning abilities. Speech to text using AI has become a rather commonplace service with the increasing application of these models.

Use Cases for Speech Recognition

  • Voice-based Digital Assistance Providers

Today, an increasing number of consumers depend on voice-based digital  assistance, and the number will only increase in the near future. In fields like customer care and service, front desk automation, voice-based digital assistants can immensely cut down costs.

  • Natural Language Processing (NLP) Services

Speech recognition capabilities are a crucial part of NLP models. When based on AI models, speech recognition becomes more accurate and makes it easier to identify and understand the components of natural language. Further, speech recognition AI models can be used for voice recognition services, making an NLP service well-rounded and more efficient.


Thanks to the AI-support, the accuracy of speech recognition programs has increased manifold. Hence, now there is a broader range of applications available for this technology, such as voice-controlled automation in infrastructure facilities, voice-based digital assistants, and NLP.

Further, in the digital marketing sphere, speech recognition has the potential to revolutionize how you build your brand value by giving the art of storytelling a whole new dimension.