The pain of transcribing audio can be understood by content marketers and professionals who rely on transcripts in their work. Audio transcripts are widely used for/by customer success story writers, legal transcriptionists or sub-title writers. One common question in their mind is “why there is still no software that can automatically transcribe audio and convert into text with good accuracy?”

One of the most popular audio transcribing software is the Nuance Transcription Engine, a product by Nuance Communications. In Google search, search for audio transcription software, and you will see thousands of software that can help you transcribing your audio content. However, the problem is that only if the user speaks clearly and carefully, the output text is close to accurate.

So, why is that even after decades of research and with the most advanced technologies like AI/ML, there is no such software that transcribes real-world audio with multiple voices and background noises accurately?

Each person has got their way of speaking

Let’s take English, for example, which is a widely spoken language, but it sounds different in various parts of the world. The significant difference between British English and American English is in pronunciation. Some words and grammar usage also vary. Setting aside the regional differences, if we further break it down to communities and individual, each person has a different way of speaking. With these variations, training a machine to recognize human voice becomes tough despite being the most widely spoken language. What makes it even worse is when the same person speaks differently in different situations.

Will there be a breakthrough? 

This is a question which needs to be answered by the machine learning/ deep learning experts, but until then, audio transcription is time and resource-intensive. As per reports, Google, Apple, Microsoft, to name a few are actively working on using deep learning technology to build systems that can understand human speech better. They are using deep learning to simulate the way our human brain works and is becoming a mainstream technology for speech recognition.

#audio #transcription #ai #ml