To develop a chatbot or voice assistant that can respond to user input in natural language, you will need to use natural language processing (NLP) techniques. The specific techniques you will need will depend on the functionality you want to include in your chatbot or voice assistant.

Here are some common NLP tasks that you may want to consider incorporating into your chatbot or voice assistant:

• Sentiment Analysis: To determine the sentiment or emotional tone of the user's input.

• Language Translation: To translate the user's input into a different language.

• Text Summarization: To generate a summary of the user's input.

• Intent Recognition: Identify the intent of user's input, this could include keywords and patterns, and match it with predefined intents.

• Named Entity Recognition: To identify and extract named entities such as people, organizations, and locations from the user's input.

• Text Generation: To generate responses to the user's input using a language model.

• To implement these NLP tasks, you can use pre-trained models or libraries such as Tensorflow, NLTK, spaCy, etc.

It's important to note that creating a chatbot or voice assistant that can respond to user input in natural language is a challenging task and it's recommended to have a good understanding of NLP and machine learning.

Additionally, you will need to have a good understanding of the specific domain or topic that your chatbot or voice assistant will be operating in, as well