Speech to Text

Speech to Text is an AI technology that converts spoken language into written text in real time or after the fact. The system analyzes sound waves, recognizes words and phrases, and translates them into readable text without anyone having to type along. For SMBs, this means meetings, interviews, customer service calls or podcasts are automatically transcribed, saving hours of manual typing.

How Speech to Text works and what sets it apart from simple dictation software

Modern speech-to-text systems use machine learning and neural networks to interpret spoken language. The process begins with recording audio, after which the system breaks the sound wave down into small fragments. Each fragment is compared to millions of speech samples that the model has previously learned. This allows it to recognize not only individual words but also context, speaking pace, accents, and background noise. Unlike older dictation software that relied on dictionaries, a speech-to-text system continuously learns. It can handle colloquial language, interruptions, and even multiple speakers at the same time. Quality depends on how well the model has been trained, the clarity of the audio, and the presence of technical jargon or dialects. Systems such as Google Cloud Speech-to-Text and OpenAI Whisper are getting better and better at recognizing Dutch and Flemish, including regional pronunciations.

Why Speech to Text came about and why it is now widely available

Speech recognition is not a new idea. As early as the 1960s, researchers were experimenting with systems that could recognize spoken digits. But only with the advent of large data sets, faster processors and deep learning did Speech to Text become accurate enough for practical use. Around 2015, tech companies such as Google, Microsoft and Amazon began making their speech models available through APIs, giving smaller companies access as well. Today, the technology is built into tools such as Zoom, Microsoft Teams and Google Meet, and available as a standalone service through platforms such as Descript, Otter.ai and Whisper. Accuracy is now often above 95 percent for clear audio in standard Dutch. That makes it a useful solution for companies that produce or process a lot of spoken content.

What Speech to Text delivers for SMEs in practice

For an SME, Speech to Text can speed up various processes. Consider transcribing customer service calls for analysis, converting webinars to blog articles, or automatically taking minutes of team meetings. A recruitment agency can transcribe job interviews and make them searchable. A consulting firm can turn client interviews into reports without hours of typing. A podcast producer can have episodes converted into text for SEO optimization and accessibility. The technology often integrates with existing workflows through API links or plugins. Monkey Vision sees with customers that Speech to Text pays off especially when it becomes part of a larger automation process, such as combined with AI summarization or content generation. In this way, spoken input becomes instantly usable content.

Speech to Text is deployed in SMEs in different ways depending on the type of content and desired output. The technology is most effective when there is regular spoken input that would otherwise have to be worked out manually. Below are four specific applications that we often see in practice, plus a decision framework when Speech to Text is or is not the right choice.

Automatic meeting and call taking

Many companies lose time working out meeting minutes. With Speech to Text, you can have meetings transcribed directly through tools such as Otter.ai, Microsoft Teams or Google Meet. The text is automatically generated and can be edited or summarized immediately. For a team of 10 employees that meets for two hours weekly, this quickly saves five hours of typing per month. The transcription is searchable, allowing you to quickly find later who said what. Note that the quality depends on the sound quality and the number of speakers. With a lot of background noise or talking through each other, accuracy decreases. A good microphone and structured meetings significantly improve results.

Content creation for SEO and accessibility

Podcasts, webinars, and videos contain valuable content, but they aren’t directly readable by search engines. By transcribing audio to text, you make that content discoverable. A 45-minute podcast episode yields about 6,000 to 8,000 words, enough for several blog posts or a knowledge base page. Companies such as Monkey Vision use Speech to Text to convert video interviews with customers into case studies or social media posts. In addition, transcription improves accessibility for people with hearing impairments. According to the Dutch Data Protection Authority, this also contributes to inclusive design and GDPR compliance for public content. Editing raw transcripts into readable text still requires some editorial work, but it’s faster than starting from scratch.

Customer service analysis and quality assurance.

Companies that have a lot of telephone contact can use Speech to Text to transcribe and analyze conversations. Think of an insurance company, a help desk or a B2B supplier. By searching transcriptions by keywords, you can discover patterns: what questions recur frequently, what do customers encounter, how do employees react. This provides input for training, FAQs and product improvements. Some systems link speech recognition to sentiment analysis, so you can also measure emotions. Be aware of privacy laws: customers must give permission for recording and transcription. Anonymize personal data before sharing or analyzing transcripts. At Monkey Vision , we see that this application works primarily for companies with more than 20 phone calls per week.

When Speech to Text is the right choice and when it is not

Speech to Text pays off when you regularly produce spoken content that you want to reuse, search or make accessible. It works well for clear audio, structured conversations and standard language. It works less well with technical jargon, dialects, poor audio or chaotic group conversations with lots of interruptions. If you only have a meeting once a month, manual note-taking is often faster. If you have weekly interviews, webinars or customer service calls, automation is worthwhile. Choose a system that fits your language and industry: some models are trained on medical or legal jargon, others on general conversational language. Test with a free version first before investing in a paid subscription.