What Is Speech Recognition?

April 17, 2023
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What Is Speech Recognition?

The human voice allows people to express their thoughts, emotions, and ideas through sound. Speech separates us from computing technology, but both similarly rely on words to transform ideas into shared understanding. In the past, we interfaced with computers and applications only through keyboards, controllers, and consoles—all hardware. But today, speech recognition software bridges the gap that separates speech and text.

First, let’s start with the meaning of automatic speech recognition: it’s the process of converting what speakers say into written or electronic text. Potential business applications include everything from customer support to translation services.

Now that you understand what speech recognition is, read on to learn how speech recognition works, different speech recognition types, and how your business can benefit from speech recognition applications.


How does speech recognition work?

Speech recognition technologies capture the human voice with physical devices like receivers or microphones. The hardware digitizes recorded sound vibrations into electrical signals. Then, the software attempts to identify sounds and phonemes—the smallest unit of speech—from the signals and match these sounds to corresponding text. Depending on the application, this text displays on the screen or triggers a directive—like when you ask your smart speaker to play a specific song and it does.

Background noise, accents, slang, and cross talk can interfere with speech recognition, but advancements in artificial intelligence (AI) and machine learning technologies filter through these anomalies to increase precision and performance.

Thanks to new and emerging machine learning algorithms, speech recognition offers advanced capabilities:

  • Natural language processing is a branch of computer science that uses AI to emulate how humans engage in and understand speech and text-based interactions.
  • Hidden Markov Models (HMM) are statistical models that assign text labels to units of speech—like words, syllables, and sentences—in a sequence. Labels map to the provided input to determine the correct label or text sequence.
  • N-grams are language models that assign probabilities to sentences or phrases to improve speech recognition accuracy. These contain sequences of words and use prior sequences of the same words to understand or predict new words and phrases. These calculations improve the predictions of sentence automatic completion systems, spell-check results, and even grammar checks.
  • Neural networks consist of node layers that together emulate the learning and decision-making capabilities of the human brain. Nodes contain inputs, weights, a threshold, and an output value. Outputs that exceed the threshold activate the corresponding node and pass data to the next layer. This means remembering earlier words to continually improve recognition accuracy.
  • Connectionist temporal classification is a neural network algorithm that uses probability to map text transcript labels to incoming audio. It helps train neural networks to understand speech and build out node networks.

Features of speech recognition

Not all speech recognition works the same. Implementations vary by application, but each uses AI to quickly process speech at a high—but not flawless—quality level. Many speech recognition technologies include the same features:

  • Filtering identifies and censors—or removes—specified words or phrases to sanitize text outputs.
  • Language weighting assigns more value to frequently spoken words—like proper nouns or industry jargon—to improve speech recognition precision.
  • Speaker labeling distinguishes between multiple conversing speakers by identifying contributions based on vocal characteristics.
  • Acoustics training analyzes conditions—like ambient noise and particular speaker styles—then tailors the speech recognition software to that environment. It’s useful when recording speech in busy locations, like call centers and offices.
  • Voice recognition helps speech recognition software pivot the listening approach to each user’s accent, dialect, and grammatical library.

5 benefits of speech recognition technology

The popularity and convenience of speech recognition technology have made speech recognition a big part of everyday life. Adoption of this technology will only continue to spread, so learn more about how speech recognition transforms how we live and work:

  1. Speed: Speaking with your voice is faster than typing with your fingers—in most cases.
  2. Assistance: Listening to directions from users and taking action accordingly is possible thanks to speech recognition technology. For instance, if your vehicle’s sound system has speech recognition capabilities, you can tell it to tune the radio to a particular channel or map directions to a specified address.
  3. Productivity: Dictating your thoughts and ideas instead of typing them out, saves time and effort to redirect toward other tasks. To illustrate, picture yourself dictating a report into your smartphone while walking or driving to your next meeting.
  4. Intelligence: Learning from and adapting to your unique speech habits and environment to identify and understand you better over time is possible thanks to speech recognition applications.
  5. Accessibility: Entering text with speech recognition is possible for people with visual impairments who can’t see a keyboard thanks to this technology. Software and websites like Google Meet and YouTube can accommodate hearing-impaired viewers with text captions of live speech translated to the user’s specific language.

Business speech recognition use cases

Speech recognition directly connects products and services to customers. It powers interactive voice recognition software that delivers customers to the right support agents—each more productive with faster, hands-free communication. Along the way, speech recognition captures actionable insights from customer conversations you can use to bolster your organization’s operational and marketing processes.

Here are some real-world speech recognition contexts and applications:

  • SMS/MMS messages: Write and send SMS or MMS messages conveniently in some environments.
  • Chatbot discussions: Get answers to product or service-related questions any time of day or night with chatbots.
  • Web browsing: Browse the internet without a mouse, keyboard, or touch screen through voice commands.
  • Active learning: Enable students to enjoy interactive learning applications—such as those that teach a new language—while teachers create lesson plans.
  • Document writing: Draft a Google or Word document when you can't access a physical or digital keyboard with speech-to-text. You can later return to the document and refine it once you have an opportunity to use a keyboard. Doctors and nurses often use these applications to log patient diagnoses and treatment notes efficiently.
  • Phone transcriptions: Help callers and receivers transcribe a conversation between 2 or more speakers with phone APIs.
  • Interviews: Turn spoken words into a comprehensive speech log the interviewer can reference later with this software. When a journalist interviews someone, they may want to record it to be more active and attentive without risking misquotes.

Try Twilio’s Speech Recognition API

Speech-to-text applications help you connect to larger and more diverse audiences. But to deploy these capabilities at scale, you need flexible and affordable speech recognition technology—and that’s where we can help.

Twilio’s Speech Recognition API performs real-time translation and converts speech to text in 119 languages and dialects. Make your customer service more accessible on a pay-as-you-go plan, with no upfront fees and free support. Get started for free!