Speech-to-Text (STT)

The core problem speech-to-text solves is simple: human time is expensive, and transcribing audio manually is slow and error-prone. Imagine trying to manually type out every customer service call, sales conversation, or podcast episode your company produces. You'd need armies of transcribers, and you'd still miss insights buried in hours of audio.

Think of speech-to-text like having a highly trained note-taker in every meeting. The technology listens to audio, recognizes patterns in speech (accounting for accents, background noise, industry jargon), and converts it into text. Modern systems use artificial intelligence to understand context—so "their" and "there" get used correctly, and technical terms stay accurate.

In marketing tools, speech-to-text shows up everywhere: Zoom automatically transcribes meetings, customer interview platforms capture and index spoken feedback, and voice-search optimization tools analyze how people actually talk about your products. Gong and similar conversation intelligence platforms use STT to turn sales calls into searchable records, then AI analyzes them for coaching insights.

The practical implication for buying AI tools is this: any platform claiming to analyze customer conversations, sales calls, or video content must have accurate speech-to-text built in. Poor transcription accuracy means poor analysis. When evaluating tools, test their STT accuracy on your actual content—industry jargon, accents, and background noise matter. Also check whether the system learns your brand's terminology over time, or if it stays generic.

For marketing leaders, STT unlocks the ability to scale qualitative research. Instead of listening to 50 customer interviews manually, you can search transcripts, run sentiment analysis, and identify themes across hundreds of conversations in hours instead of weeks.