February 24, 2026

Speech-to-Text AI for product managers: How it works and key considerations

Learn how speech-to-text AI technology works and read about key considerations when weighing your options.

Julie Griffin

Featured writer

Product Management

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Speech-to-text, also known as Automatic Speech Recognition (ASR), is exactly what it sounds like—converting spoken words into written words. Though speech-to-text is a simple concept, the AI technology behind it is robust and has grown exponentially, as market projections show the field is expected to reach a market volume of US$73 billion by 2031. Learn how speech-to-text works, explore the different types of solutions available, and discover key considerations when evaluating your options.

What is speech-to-text?

At its core, speech-to-text technology, also known as Automatic Speech Recognition (ASR), is a process that converts spoken language into written text. Think of it as a bridge between the way we talk and the way we write, making voice data readable, searchable, and analyzable.

While the concept is simple, the underlying AI models are incredibly complex. They take an audio input, break it down into individual sounds, and use advanced algorithms to predict the most likely sequence of words. This capability unlocks the value hidden in audio and video files, from customer calls to podcast episodes.

How does speech-to-text AI work?

Most modern speech-to-text methods involve End-to-End Deep Learning to directly route an acoustic waveform into a sequence of words. This trend is widespread; in the related field of clinical NLP, for example, one survey found that publications on deep learning more than doubled each year through 2018. Large quantities of data are required to train these AI models for accurate transcriptions.

Key requirements for effective speech-to-text AI:

Extensive training data: Without sufficient training, transcriptions become less accurate and useful
Continuous innovation: Speech-to-Text AI technology built by expert researchers who deploy new neural networks
Regular updates: Without constant improvements, you risk leveraging outdated AI models

Now that you have an idea of how Speech-to-Text AI technology works and the importance of selecting one that is high-quality, let's explore the different types of solutions available.

Types of speech-to-text solutions

Speech-to-text technology comes in three main solution types, each designed for different technical requirements and use cases:

APIs for Developers: These are for builders who want to integrate speech-to-text directly into their own applications. An API, like the one we offer at AssemblyAI, provides access to powerful AI models without needing to build them from scratch. This is the most flexible and scalable option for creating custom voice experiences.
End-User Software: These are ready-to-use applications designed for transcription. You upload an audio or video file, and the software returns a text transcript. They're great for individuals or teams who need to transcribe content without any coding.
On-Premise Solutions: For organizations with strict data security or privacy requirements, on-premise solutions allow them to host the speech-to-text models on their own infrastructure. This gives them full control over their data but requires significant technical overhead to manage.

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Common use cases and applications

Speech-to-text technology drives measurable business value across four key application areas:

Call Center Analytics: Companies like CallSource and Ringostat use speech-to-text to transcribe and analyze customer service calls, understanding sentiment and tracking performance at scale. In one case study, lead intelligence company CallRail increased its call transcription accuracy by up to 23% and doubled the number of customers using its conversation intelligence product after integrating Speech AI.
Media and Content Creation: Platforms like Veed and Podchaser generate accurate captions and subtitles, making content more accessible and discoverable.
Meeting Intelligence: AI notetakers from companies like Recall automatically transcribe virtual meetings, creating searchable records so teams focus on conversation instead of notes. The time savings can be dramatic, as shown in a recent case study where an AI scribe for mental health professionals led to a 90% reduction in documentation time.
Voice-Enabled Experiences: From in-app voice commands to dictation features, developers build more natural and hands-free user interfaces.

What to look for in speech-to-text AI

When evaluating speech-to-text AI technology, focus on seven key criteria that determine performance and business value:

Near Human-Level Accuracy

Transcription accuracy is one of the most important qualities of speech-to-text software. If the transcription is inaccurate and changes the meaning of what is said, then the user has to go back to the audio to better interpret the context of the conversation. Accuracy ensures that the user saves time using speech-to-text software.

When looking at speech-to-text models, the accuracy should be as close to human level as possible. Also check to see if the AI model has an array of valuable features like:

Automatic punctuation, casing, and alphanumerics: Automatically add the casing of proper nouns and have the model incorporate punctuation for natural sentences, listicles, and alphanumerics.
Speaker diarization: Detect the number of speakers within the audio file and associate each word within the transcript to a speaker. This can be incredibly helpful for calls that have several speakers.
Noise robustness: Accurately transcribe audio with background noise, a key feature of state-of-the-art models like AssemblyAI's Universal model.
Confidence scores: Receive a confidence score for each word within the transcript. A low confidence score can tell a user that the word may have been interpreted incorrectly. The client program can then create a logic to handle low confidence words depending on the application scenario it serves.

Customization and Spoken Language Understanding with LLMs

Customization features can help businesses personalize the speech-to-text software for their use cases. For example, if a business has custom terms, such as the name of the business, products or features, it can be helpful to note specific spellings or vocabulary for the speech-to-text AI model to process.

Custom spelling: Customize how words are spelled or formatted in the transcription text.
Keyterms Prompting: Boost the accuracy of your transcripts by providing a list of important words or phrases unique to your business in your API request.
Profanity filtering: Automatically detect and replace profanity within the transcription text.

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Open playground

You'll also want to see if the speech-to-text AI solution has additional features you can incorporate. For example, given that an industry survey found that over 30% of product leaders cite data privacy as a significant challenge, features like PII Redaction are crucial to help businesses automatically remove personally identifiable information from text transcripts and audio files.

Additionally, by pairing speech-to-text APIs with Large Language Model (LLM) frameworks, businesses can build LLM apps on spoken data that search, summarize, and generate text with your spoken content.

Multiple Languages

If you're building for an international audience, you'll need broad language support. Look for an AI model that supports a wide range of languages, like AssemblyAI's Universal-2 model which supports 99 languages.

You may also want to look for automatic language detection, which can identify the dominant language spoken in an audio file and automatically route it to the appropriate model for that language.

Transcription Speed

When you're working with large quantities of audio files, speed becomes essential. Look for an asynchronous transcription API that can process audio much faster than real-time. For example, AssemblyAI's latest models can transcribe a one-hour file in just a few minutes. In addition to asynchronous transcription, consider a real-time transcription API with high accuracy and low latency so you'll get results in a matter of milliseconds.

Consistent Innovation, Updates, and Ease of Integration

Is there a team of engineers constantly working through bugs, improving accuracy, and developing the latest and greatest enhancements? AI technology is changing rapidly, and if there isn't a team focused on improving and innovating with the software, then the software likely isn't a good long-term solution.

Look for a solution with a dedicated engineering team as well as dedicated resources. Check to make sure the solution has weekly product and accuracy improvements, extensive documentation, and video tutorials to ensure there's ease of use for developers.

Ability to Scale as Your Business Grows

Another consideration when weighing your speech-to-text API options is its ability to scale. Here are a few questions to consider:

Does the technology have the bandwidth to process thousands (even millions) of files? You may not need this quantity of transcription currently, but you may down the line.
Does it offer in-house support? As the business grows, you may need to lean on AI experts for additional support.
What is the uptime? Look for models that offer 99.9% uptime, so you can build with confidence.
Is the software following security best practices? A company that prioritizes security, such as SOC 1 and 2 compliance and third party audits, offers peace of mind that the audio you're transcribing is protected.

If you're looking for a solution that scales with you, look for one that can process millions of files daily, has 24/7 support from support engineers and technical account managers, has 99.9% uptime and enterprise-grade security.

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Free Speech-to-Text Software vs Paid Plans

One of the biggest considerations is cost. There are a few free speech-to-text options on the market which can be a great solution if you're looking to test how speech-to-text can enhance your business.

However, if you're looking for a long-term solution that can handle hundreds of thousands of hours of audio with high accuracy, then a free solution may not be the right fit. Free speech-to-text solutions also require more legwork on your end to tailor the toolkit to your needs.

If you're unsure whether a paid plan is worth it, look for a free trial, free tier or speech-to-text playground to test the speech-to-text software first.

Getting started with speech-to-text implementation

Understanding how speech-to-text works and what to look for in a solution is the first step. But the best way to truly grasp its potential is to see it in action. Whether you're looking to analyze call recordings, caption videos, or build the next great voice-enabled app, the quality of the underlying AI model will define your user's experience.

If you're a developer, you can start building and testing our models today. Try our API for free and see what you can create with your voice data.

Frequently asked questions about speech-to-text

What's the difference between speech-to-text and voice recognition?

Speech-to-text converts spoken words into text, while voice recognition is broader and includes identifying speakers or understanding commands without full transcription.

How accurate is modern speech-to-text technology?

The best speech-to-text AI models achieve near-human accuracy on clean audio, measured by Word Error Rate (WER), though real-world conditions affect performance.

What factors affect speech-to-text accuracy?

Key factors include background noise, microphone quality, accents, industry jargon, and overlapping speakers, with audio quality being the most significant factor.

What's the difference between real-time and batch processing?

Batch processing transcribes pre-recorded files completely, while real-time processing generates transcripts continuously for live audio in milliseconds.

Do I need an internet connection for speech-to-text?

Most solutions require internet for cloud-based APIs, though on-premise deployment options allow offline operation for maximum data security.