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

Moderne Speech to Text-systemen gebruiken machine learning en neurale netwerken om gesproken taal te interpreteren. Het proces begint met het opnemen van audio, waarna het systeem de geluidsgolf opsplitst in kleine fragmenten. Elk fragment wordt vergeleken met miljoenen spraakvoorbeelden die het model eerder heeft geleerd. Zo herkent het niet alleen losse woorden, maar ook context, spreektempo, accenten en achtergrondgeluiden. In tegenstelling tot oudere dicteersoftware die woordenboeken gebruikte, leert een Speech to Text-systeem continu bij. Het kan omgaan met spreektaal, onderbrekingen en zelfs meerdere sprekers tegelijk. Kwaliteit hangt af van de training van het model, de helderheid van de audio en de aanwezigheid van vakjargon of dialecten. Systemen zoals Google Cloud Speech-to-Text en OpenAI Whisper worden steeds beter in het herkennen van Nederlands en Vlaams, inclusief regionale uitspraak.

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 en video's bevatten waardevolle content, maar zijn voor zoekmachines niet direct leesbaar. Door audio om te zetten naar tekst maak je die content vindbaar. Een podcast-aflevering van 45 minuten levert zo'n 6.000 tot 8.000 woorden op, genoeg voor meerdere blogartikelen of een kennisbankpagina. Bedrijven zoals Monkey Vision gebruiken Speech to Text om video-interviews met klanten om te zetten in case studies of social media posts. Daarnaast vergroot transcriptie de toegankelijkheid voor mensen met een gehoorbeperking. Volgens de Autoriteit Persoonsgegevens draagt dit ook bij aan inclusief ontwerp en AVG-compliance bij publieke content. De bewerking van ruwe transcripties naar leesbare tekst vraagt nog wel redactioneel werk, maar dat gaat sneller dan vanaf nul beginnen.

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.