For any conversation with more than one person, you need to know who said what. Speaker diarization (or speaker labels) identifies each unique speaker in the audio and attributes each part of the transcript to them.
This is where Voice AI moves beyond simple transcription. Speech understanding models analyze the transcribed text to extract valuable insights. Key capabilities include:
To ensure safe and compliant applications, modern APIs include guardrails to manage sensitive content. Key capabilities include:
Speech recognition applications today reach far beyond just dictation software. In fact, AI speech recognition technology is powering a wide range of versatile Voice AI use cases across numerous industries.
Speech recognition is being used as the foundation for powerful Conversation Intelligence platforms and to augment call centers, voice assistants, chatbots, and more. Conversation Intelligence platforms, for example, transcribe calls using speech recognition models and then apply additional Voice AI models to this data to analyze calls at scale, automate personalized responses, coach customer service representatives, identify industry trends, and more. Combined, these Voice AI tools create a better overall user experience.
The healthcare industry uses speech recognition technology to transcribe both in-office and virtual patient-doctor interactions. Additional Voice AI models are then used to perform actions such as redacting sensitive information from medical transcriptions and auto-populating appointment notes to reduce doctor burden. For example, one study found that an ambient AI tool decreased the time clinicians spent in electronic medical records from 90.1 to 70.3 minutes per day.
K-12 school systems and universities are implementing speech recognition tools to make online learning more accessible and user-friendly. Learning management systems, or LMSs, are adding speech-to-text transcription to increase the accessibility of course materials, as well as building with additional Voice AI models that can catalog course content, help educators evaluate reading comprehension, augment feedback loops, and more.
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Content creation
Not surprisingly, speech recognition models are also being used by the content creation community. Tools like AI subtitle generators help creators more easily add AI-generated subtitles to their videos, as well as allow them to modify how the subtitles are displayed (color, font, size, etc.) on the video itself. The addition of subtitles makes the videos more accessible and increases their searchability to generate more traffic.
Smart homes and IoT
Smart home devices, like Google Home and Nest, have also integrated speech recognition technology to allow for a more seamless user experience. Accuracy is especially important for these devices, as well as IoT devices, as users need to interact with the technology via voice commands and receive timely responses.
Automotive
Speech recognition technology is also being integrated directly into vehicles to power navigational voice commands and in-vehicle entertainment systems.
Benefits of speech recognition: A game-changer for productivity and accessibility
Speech recognition technology offers a multitude of benefits across industries: increased productivity, improved operational efficiency, better accessibility, enhanced user experience, and more.
Jiminny, a leading conversation intelligence, sales coaching, and call recording platform, uses speech recognition to help customer success teams more efficiently manage and analyze conversational data. The insights teams extract from this data help them finetune sales techniques and build better customer relationships — and help them achieve a 15% higher win rate on average.
Qualitative data analysis platform Marvin built tools on top of speech recognition and Voice AI to help its users spend 60% less time analyzing data, significantly boosting productivity.
Screenloop, a hiring intelligence platform, integrated AI speech recognition to transcribe and analyze interview data. In addition to reduced time-to-hire and fewer rejected offers, Screenloop users spend 90% less time on manual hiring and interview tasks.
Lead intelligence company CallRail was an early adopter of speech recognition and Voice AI. Since its integration, its AI-powered conversation intelligence tools have increased call transcription accuracy by up to 23%. The company also doubled the number of customers using its conversation intelligence product.
Choosing the right speech recognition API: A buyer's guide
Choosing the best Speech-to-Text API or AI model for your project can seem daunting, but here are a few considerations to keep in mind.
1. Accuracy
Accuracy is one of the most important comparison tools we have for speech recognition APIs. Word Error Rate, or WER, is a good baseline to use when comparing, but keep in mind that the types of audio files (noisy versus academic settings, for example) will impact the WER. In addition, always look for a publicly available dataset to ensure the provider is offering transparency and replicable results — the absence of this would be a red flag.
WER does have limitations, however, as it can still be difficult to assess the "readability" of the text. Diffchecker tools — tools that allow you to compare two blocks of text and eyeball the differences for quick comparison — can be helpful here.
2. Additional features and models
In addition to speech recognition, it can be helpful when a provider offers additional Natural Language Processing and Speech Understanding models and features, such as LLMs, Speaker Diarization, Summarization, and more. This will enable you to move beyond basic transcription and into AI analysis with greater ease.
3. Support
Building with AI can be tricky. Knowing that you have a direct line of communication with customer success and support teams while you build will ensure a smoother and faster time to deployment. Also consider a company's uptime reports, customer reviews, and changelogs for a more complete picture of the support you can expect.
4. Documentation
API documentation should be readily accessible and easy to follow, helping you get started with speech recognition faster. Quickstart guides, code examples, and integrations like SDKs will all be helpful resources, so ensure their availability prior to starting a project.
5. Pricing
Transparent pricing is also a necessity so that you can get an accurate idea of your incurring costs prior to building. Watch out for hidden costs and check for bulk usage discounts to save in the long term.
6. Language support
If you need multilingual support, make sure you check that the provider offers the language you need. Automatic Language Detection (ALD) is another great tool, as it automatically detects the dominant language in an audio or video file for accurate transcription. Translation into other languages is handled by a separate model after transcription.
7. Privacy and security
When dealing with large amounts of sensitive data, solid privacy and security practices are a must. An industry survey confirmed this, finding that over 30% of respondents cited data privacy and security as a significant challenge when building with speech recognition. Make sure your speech recognition provider can answer questions such as:
- Have I accounted for defense in depth?
- Does the API provider adhere to strict industry standard frameworks?
- How much transparency is provided in code-level controls?
- What technical controls are supporting the security of my data?
Additionally, privacy measures like Personally Identifiable Information (PII) redaction ensure that data in particularly sensitive fields like medicine and customer information remains private.
8. Innovation
The fields of speech recognition and Voice AI are in nearly constant innovation. When choosing an API, make sure the provider has a strong focus on AI research and a history of frequent model updates and optimizations. This will ensure your speech recognition tool remains state-of-the-art.
The future of speech recognition: A glimpse into the voice-enabled world
Advancements in speech recognition and Voice AI continue to accelerate. Expect accuracy to continue to improve, as well as support for multilingual speech recognition and faster streaming, or real-time, speech recognition.
We'll also see new applications for speech recognition expand in different areas. Voice biometrics, for example, is a technology that uses a person's voice "print" to identify and authenticate them, and is already being integrated into technology like banking over the phone. Emotion recognition uses AI to detect human emotions in spoken audio or video as well as using facial detection technology.
In general, we can expect speech recognition technology to be integrated into nearly every aspect of daily life — from grocery checkouts to self-driving cars to home applications.
Still, some concern remains over the responsible use of speech recognition technology, especially over data privacy, data security, and biases in AI algorithms, with well-documented cases like Amazon's biased hiring algorithm highlighting the risks. Open conversations with AI providers will help assuage some of these concerns, as well as assess their commitment to responsibly move the field forward.
Getting started with Voice AI
Speech recognition has evolved into a foundational technology for building intelligent applications—from improving customer service to making media accessible. You can explore transcription and speech understanding features with a free API key. Try our API for free.
The best way to understand the power of modern speech recognition is to see it in action. By integrating a Voice AI API, you can start transcribing and understanding audio data in minutes, not months. This allows you to focus on building your core product while leveraging the latest advancements from a dedicated team of AI researchers.
Frequently asked questions about speech recognition
What's the difference between speech recognition and voice recognition?
It's a common point of confusion. Speech recognition determines what was said by converting spoken words into text. Voice recognition, also known as speaker identification or biometrics, determines who is speaking by analyzing their unique voice print.
How accurate is speech recognition?
Modern AI-powered speech recognition achieves near-human accuracy on clean audio, with top-tier APIs maintaining high performance despite background noise, accents, and audio quality issues.
Can speech recognition work in real-time?
Yes, through streaming speech-to-text technology that transcribes speech as it's spoken with very low latency.
