To use AI to recognize a person in an anonymized dataset based on speech patterns, one approach is to use an artificial neural network (ANN) to analyze the social behavior of individuals in the dataset[1]. Here are some steps to follow:

1. Collect the anonymized dataset containing information on anonymous users’ interactions.
2. Organize each user’s interaction data into web-shaped data structures consisting of nodes representing the user and their contacts. Strings threaded with interaction data connect the nodes.
3. Train an ANN to recognize the social behavior of individuals in the dataset. The ANN should be shown the interaction web of a known person and then set loose to search the anonymized data given not just information about the target’s interactions but also those of their contacts[1].
4. Test the ANN’s ability to identify individuals in the anonymized dataset by providing it with the interaction data of a known person and seeing if it can correctly identify that person in the dataset[1].

Another approach is to use natural language processing (NLP) to anonymize the data while preserving its usefulness for analysis. This involves using NLP techniques to detect different types of entities in free text, such as phone numbers or names, and then replacing them with anonymized placeholders[2].

It’s worth noting that there are also many other applications of AI in speech analysis, such as detecting Parkinson’s disease from nocturnal breathing signals[3].

Citations:

[1] https://www.sciencenews.org/article/ai-identify-anonymous-data-phone-neural-network

[2] https://towardsdatascience.com/nlp-approaches-to-data-anonymization-1fb5bde6b929

[3] https://www.nature.com/articles/s41591-022-01932-x

By Perplexity at https://www.perplexity.ai/search/393f6b31-7a52-4e71-b9c8-d58ef6d14bf7