Speech to Text conversion is a bit old technologies but usage of this in larger business context is wide. Especially if you are dealing with terabytes of audio and video data and its analytics.
It has more value addition if we have analyse, tag or convert in to multi-lingual like English to Spanish, especially using deep learning technologies.
By Deep Learning, either using self build AI model or using cloud services providing multi-lingual conversion support is becoming very popular.
Deep learning has totally rewritten our approach to machine translation. Deep learning researchers who know almost nothing about language translation are throwing together relatively simple machine learning solutions that are beating the best expert-built language translation systems in the world. The technology behind this breakthrough is called sequence-to-sequence learning. This is easy to implement because all you need is a dictionary to look up each word’s translation. But the results are bad because it ignores grammar and context. After the failure of rule-based systems, new translation approaches were developed using models based on probability and statistics instead of grammar rules.
Most of deep learning based translation uses following steps:-
Step 1: Break original sentence into chunks
Step 2: Find all possible translations for each chunk
Step 3: Generate all possible sentences and find the most likely one
Two big ideas make this possible — recurrent neural networks and encodings. By combining these two ideas in a clever way, we can build a self-learning translation system.
Benefits of Machine Translation :
- Faster and automated process to build and distribute audio/video/text/article/news/learning materials etc
- Cost avoidance and good ROI
- If used cloud based services, its pay as you and nothing to worry about big implementation and maintenance cost
- Great for enterprise search to support multi-lingual
- Voice based search and data retrieval