Top 11 Voice Recognition Applications & Examples
If you’ve used virtual assistants like Alexa, Cortana, or Siri, you’re likely familiar with speech recognition and conversational AI. This technology enables users to interact with devices through verbal commands by converting spoken queries into machine-readable text. The number of voice assistant users in the U.S. is projected to increase from 145 million in 2023 to 170 million in 2028, showing a compound annual growth rate of almost 3%.1 This trend reflects rising interest and the potential of the voice recognition market.
We reviewed the top 11 uses of voice recognition technology in marketing, customer service, healthcare, and other areas.
Common applications
1. Voice search
Voice search is arguably the most common application of voice recognition. By the end of 2025, 153 million Americans are expected to use voice assistants, up from 142 million in 2022, a 8.1% increase.2
Real-life example:
Approximately 20% of the global population utilizes voice search, while the number of voice assistants, at around 8 billion, exceeds the total world population. In the U.S., 153 million individuals depend on voice assistants, with Google Assistant projected to be the most popular, reaching 92 million users by 2025. Typical questions include “Hey Google, what’s today’s weather?” as smart devices access online information to provide accurate weather updates.3
2. Speech-to-text
Voice recognition enables hands-free computing across various applications, including writing emails, creating documents in Google Docs, generating automatic closed captions (such as on YouTube), providing automatic translations, and sending texts.
Real-life example:
Microsoft Azure’s real-time speech-to-text feature leverages call center agent support, captioning, voice-enabled interactive response systems, and live meeting transcriptions.
See the speech-to-text benchmark to find out which product to choose.
3. Voice commands to smart home devices
Smart home devices utilize voice recognition technology to automate household tasks, such as turning on lights, boiling water, adjusting thermostats, and more. Some voice recognition applications also offer additional features, such as advanced voice commands or expanded language support, enhancing their functionality and user experience.
In 2024, approximately 70 million households in the US actively used smart home devices, up from 63 million in 2023, a 10% increase. The smart home market in the US is expected to continue expanding at a 10% annual rate through 2028, with projections of 77 million households in 2025, 85 million in 2026, and 94 million in 2027.4
Real-life example:
Amazon Alexa is connected to over 400 million smart home devices, which customers operate multiple times a week. Alexa now supports over 100,000 devices, up from just 4,000 in 2017. Users can say commands like “Turn on the bedroom,” “Dim the office lamp,” “Turn on the living room lights for five minutes,” “Set the bedroom lights to 50 percent,” or “Turn the desk lamp green” for color changes.5
Business function applications
Voice recognition software is widely used in customer service applications. Check out customer service voice recognition applications, pre-sales automation applications that can be automated via voice recognition services, and top AI sales assistant software.
4. Voice biometrics for security
Similar to how your smartphone allows you to unlock it with your fingerprints, vocal biometrics uses a person’s speech to authenticate them. Users might be required to say their name aloud during log-ins rather than typing a password.
Alternatively, speech biometrics can be used in Fintech to authorize transactions and verify that they are genuine and authorized by the account owner. Besides, speech biometrics can restrict access to authorized personnel in healthcare, where maintaining patient confidentiality is of utmost importance.
Real-life example:
HSBC used speech recognition systems to identify clients by their voices, enabling secure account access without PINs or traditional passwords. This technology analyzes distinctive vocal traits, such as pitch, tone, and speech patterns, to generate a unique “voiceprint” for each individual. 6
5. Automotive
In-car speech recognition systems are now standard in most modern vehicles. The most significant benefit of car speech recognition is that it enables the driver to keep their eyes on the road and hands on the wheel. Use cases include initiating phone calls, selecting radio stations, setting up directions, and playing music.
In 2023, the in-car voice assistant market was valued at nearly $22 billion, with projections estimating it will reach $64 billion by 2031. Similarly, the automotive voice recognition system market was valued at over $3 billion in 2024 and is expected to grow to $13 billion by 2034. 7
Real-life example:
Tesla developed voice bots that allow users to manage climate, entertainment, and navigation via voice commands such as “Set temperature to 72 degrees” or “Navigate to [destination].”8
6. Academic
80% of sighted children’s learning is through vision, and their primary motivator is to explore their environment. Speech recognition can create an equitable learning platform for children with no/low sight. 9
Students and educators can also benefit from taking a course on speech recognition technology to understand its applications better and maximize its use in educational settings.
Real-life example:
Language learning tools such as Duolingo use speech recognition to evaluate users’ language pronunciation. Pronunciation evaluation is a practical computer-aided language learning application.
7. Media/marketing
Speech recognition tools, such as dictation software, allow people to write more words in less time. A study conducted by doctors using dictation software found that they could write 150 words a minute with the tool.
Therefore, content creators writing articles, speeches, books, memos, or emails can transcribe 3000 to 4000 words in 30 minutes using these applications. While these tools aren’t 100% accurate, they are effective for creating first drafts. Advanced dictation and transcription tools can also significantly reduce time editing by minimizing manual corrections and streamlining the content creation process.
Real-life example:
Whirlpool integrated speech technology into its marketing strategy by collaborating with Amazon to develop innovative, voice-activated appliances that can communicate with consumers, respond to inquiries, and offer cooking advice, including suggestions for ingredients. 10
8. Healthcare
MD note-taking
During patient examinations, doctors shouldn’t worry about taking notes on patients’ symptoms. Patient diagnosis notes are transcribed using medical transcription (MD) software powered by speech recognition.
It has been noted that taking notes is one of the most time-consuming activities for physicians, detracting from their ability to see patients. Thanks to MD technology, doctors can reduce the average appointment duration and, in turn, accommodate more patients in their schedules.
Diagnosis
Depression speech recognition technology analyzes a patient’s voice to detect the presence or absence of depression undertones through words such as “unhappy,” “overwhelmed,” “bored,” “feeling void,” etc.11
Real-life example:
Sonde Health has developed mobile applications that provide users with a score of “mental fitness” based on their voice’s tone, choice of words, energy, fluctuations, rhythm, and other factors.
9. Legal tech
Legal chatbots have grown in popularity because of their ease of use and broad applicability. Speech-enabled legal tech can expand the use cases to
- Court reporting (Realtime Speech Writing)
- eDiscovery (Legal discovery)
- Automated transcripts in depositions and interrogations
- Using NLP to review legal documents to determine if they meet regulatory criteria.
Audio transcription technology is widely used in legal settings to convert recorded depositions, interrogations, and court proceedings into accurate written records.
Real-life example:
Real-time, very accurate draft transcripts of depositions and arbitrations are produced using AI-assisted transcription systems, such as those employed by Prevail, and are subsequently refined by human transcriptionists. 12
10. Generative AI integration
Voice recognition now acts as the foundational layer for advanced AI dialogues. Voice interfaces combine speech-to-text technology with expansive language models, enabling natural, context-aware conversations that go beyond mere command inputs.
- AI writing assistants: Voice dictation now collaborates with large language models (LLMs) such as ChatGPT and Claude, enabling users to articulate their ideas, which are then enhanced, organized, and expanded by AI.
- Code generation: Developers can verbally articulate their programming needs, with AI converting these spoken guidelines into functional code.
- Voice-controlled content creation: Users can verbally outline ideas for videos, presentations, or documents, with AI producing the full content.
Real-life example:
OpenAI’s Realtime API enables integrating complex voice-based AI interactions into third-party applications, facilitating real-time “speech in, speech out” exchanges. Examples of apps that utilize the API for speech-to-speech communication include voice-activated medical assistants and AI instructors in higher education, which use six preset voices. 13
11. Multimodal voice experiences
Voice recognition is increasingly integrated with computer vision and other sensory inputs to enhance interactive experiences.
- Voice and visual search: Users can direct a camera at items while articulating their search. Smart displays respond to both verbal commands and hand gestures simultaneously.
- Contextual voice assistance: Devices leverage visual context to interpret voice commands more effectively (e.g., by recognizing “turn off that light” when the user is focusing on a specific fixture).
Real-life example:
Amazon Echo Show is a speech-enabled, multimodal interface that allows users to make voice queries and receive both visual and audio responses in a unified, seamless environment. The device synchronizes voice and visual interfaces, integrating them into the experience rather than treating them as separate elements. 14
FAQ
Further reading
- Voice/speech data collection
- Speech recognition challenges and solutions
- Conversational User Interfaces
- Voice bot platforms
If you have further questions, reach out to us:
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Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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Voice recognition tools are really helpful! As an alternative, I can recommend Audext. It works quite fast, and it has many useful features such as an in-built editor, text timings tracking, voice recognition in noise, etc.