Automatic speech recognition (ASR)

ASR enables machines to "hear" and transcribe human speech by processing audio signals and matching them to known phonemes and words. It is trained on massive datasets of audio paired with text to learn the nuances of pronunciation, accents and speed. Modern ASR systems leverage neural networks and deep learning to handle background noise, multiple speakers and complex speech patterns with increasing accuracy.

In localization and multimedia workflows, ASR is a critical efficiency tool. It serves as the first step in transforming video or audio content – providing the raw transcripts needed for subtitling, transcription and AI dubbing. By automating the transcription phase, organizations can drastically reduce the time and cost required to localize video content. When integrated with machine translation (MT), ASR enables real-time captioning and rapid multilingual content creation. While raw ASR output may require human cleanup to ensure perfect accuracy (especially for technical terminology), it provides a scalable foundation for making spoken content searchable, accessible and global.

At RWS, automatic speech recognition is an integral part of our Human + Technology approach to multimedia localization. Through Language Weaver and our Video and Audio Translation services, we combine neural ASR with human linguistic expertise.

We use ASR to do the heavy lifting – generating high-quality transcripts that feed into translation and synthesis workflows. Our linguists then refine these outputs to ensure timing, tone and terminology are flawless. The launch of Language Weaver Edge 8.7 introduced enhanced ASR capabilities that further streamline global media production, enabling faster turnaround without sacrificing authenticity. By blending automation with human oversight, we help clients unlock greater productivity and engagement from every piece of spoken content.