Definition
Speech recognition, also known as Automatic Speech Recognition (ASR), empowers computers, applications and software to understand human speech data and translate it into text for business solutions. Speech recognition model functions by utilizing artificial intelligence (AI) to analyze your voice and language, identify by studying the words you are saying, and then output those words with transcription accuracy as model content or text data on a screen [1].
Let’s take a
look at an example to get a better understanding of the concept.
Scenario: A
patient arrives at National Hospital Abuja feeling unwell and approaches their
AI-powered registration kiosk. Speaking in broken English mixed with Igbo, she
explains her condition:
Patient:
"Good morning. My belle dey pain me well well since yesterday night. I
think say na malaria or typhoid. Biko, I wan see doctor quick quick i.e. "(Good
morning. My stomach has been hurting badly since last night. I think it's
malaria or typhoid. Please, I want to see a doctor quickly.)
AI Speech
Recognition Process: Identifies health-related vocabulary ("belle
pain," "malaria," "typhoid"), handles
English-Igbo-Pidgin mixture seamlessly, recognizes "belle dey pain
me" as abdominal pain complaint, understands "quick quick"
indicates need for immediate attention, and processes "biko" (please
in Igbo) as polite request marker.
Then AI System
Responds: "I understand you have stomach pain since yesterday. I'll
register you for urgent consultation. Please provide your phone number and next
of kin details."
This example scenario shows how AI systems must understand local terminology, cultural expressions, and mixed-language communication for effective delivery.
Origin
The quest of
speech recognition started in the 1950s. But the first speech recognition
systems were only able to understand only numbers. Throughout the years, they
advanced with better vocabulary and comprehension capabilities.
By the eighties, speech recognition technology had developed to the level where it could understand limited vocabularies spoken by specific individuals. Yet, it wasn’t until the 1990s, that speech recognition gathered momentum, with the development of machine learning and artificial intelligence. These technologies enabled the development of systems that could understand large vocabularies spoken by a wide range of individuals [3].
Context and
Usage
Today, Speech
recognition technology is used across a number of industries such as in sales,
healthcare, security and in automotive, assisting to save time and even lives
for customers and businesses.
In Sales, even
without the presence of contact center agents, AI chatbots can talk to people
via a webpage, answering common queries and solving basic requests, reducing
time for resolving consumer issues.
In Healthcare, Doctors
and nurses take advantage of dictation applications to capture and log patient
diagnoses and treatment notes.
When it comes to
security, as technology becomes part of our daily lives, security protocols are
an increasing priority. Voice-based authentication adds a viable level of
security.
In Automotive, driver safety is been improved by equipping car radios with voice-activated navigation systems and search capabilities [4].
Why it Matters
Speech
recognition has been accepted as a solution to streamline work operations,
reduce reliance on manual tasks, and make jobs more efficient in many different
industries. Based on research, the market value for speech recognition technology
is expected to increase from $8.5 billion in 2024 to $19.5 billion by 2030, representing
a massive growth in demand.
Regardless of its intended use such as on a personal mobile phone or to monitor patient health in a hospital, AI and speech recognition is allowing humans and technology to work together more effortlessly, paving the way for even more emerging technologies [5].
In Practice
Aiola is a good example of a real-life case study of a company offering speech recognition services in AI. With aiOla, manual workflows can be automated using solely the power of speech. aiOla’s platform understands over 100 languages and can pick out different accents, dialects, and even industry-specific jargon to assist organizations gather critical data to inform better business decisions. Their results speak for themselves: businesses using their services saw a 90% reduction in manual operations and a 30% increase in production uptime [5].
See Also
Speech Analytics: Process of analyzing speech data to extract insights and patterns
Syntax Analysis: Understanding sentence structure
Text Analytics: Deriving insights from text
Text Summarization: Condensing content automatically
Tokens: Individual units (words, subwords, characters) that text is divided into for processing
References
- Gnani Marketing. (2022). Speech Recognition AI: What is it and How Does it Work| Gnani.
- Karatas, G. (2025). Speech Recognition: Everything You Need to Know in 2025.
- Netguru. (2025). Speech Recognition: Artificial Intelligence Explained.
- IBM. (2021). What is speech recognition?
- Amit, J. (2024). Unlocking the Power of AI Speech Recognition.