Natural language processing with neural networks
Lip-Synched
© Lead Image © lassedesignen, 123RF.com
If an actor's lip movements don't match the spoken text in a dubbed movie, it not only stresses people who are hard of hearing, but it can also make things difficult for everyone. AI can help solve this problem with lip-sync translations of movie scripts.
Automated natural language processing is an important field of artificial intelligence (AI) application. Natural language processing tasks range from text searches (such as web searches) to interaction with spoken language (such as with Siri, Alexa, or similar voice-controlled agents). Methods for intelligent language processing that go beyond the simple memorization and patterning of texts have been under development for more than 70 years. The famous Turing test [1] even considers the understanding and production of language to be a central criterion for AI.
Automated speech processing, along with speech recognition, is also one of the earliest applications for big data analytics. As early as 1952, Bell Laboratories' Audrey speech recognition system, for example, was able to set the parameters for each user such that the system recognized sequences of digits with a high degree of accuracy. Initially, this fine-tuning of the parameters had to be done manually. Automating this step takes us to machine learning, the process of adjusting model parameters based on training data.
The results of this machine learning are the models and their parameters. Accordingly, experts do not refer to AI, but instead to AI models. The widespread adoption of neural networks was a breakthrough for machine learning. Although their theoretical foundations were laid early on, more widespread use of neural networks initially required advances in computing power.
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