Voice Assistants -Interfaces, Chatbots & Natural Language Processing

Overview

In this Comprehensive Guide, we are going to be covering the following topics and the end goal is to teach you how to make your own personal intelligent AI chatbot:

  • NLP and its uses in speech interpretation.
  • AI and its uses in creating an intelligent responsive chatbot to interact with users
  • Different packages and pre-trained tools are required to create a responsive intelligent chatbot similar to virtual assistants such as ALEXA or Siri.

This comprehensive guide will cover the basic prerequisites and the steps to be covered in order to create a chatbot. You can follow along with the code snippets or modify them as per your requirements.

Introduction to AI Chatbot

Were you ever curious as to how to build a talking ChatBot with Python and also have a conversation with your own personal AI?

As the topic suggests we are here to help you have a conversation with your AI today. To have a conversation with your AI, you need a few pre-trained tools which can help you build an AI chatbot system. In this article, we will guide you to combine speech recognition processes with an artificial intelligence algorithm.

Natural Language Processing or NLP is a prerequisite for our project. NLP allows computers and algorithms to understand human interactions via various languages. In order to process a large amount of natural language data, an AI will definitely need NLP or Natural Language Processing. Currently, we have a number of NLP research ongoing in order to improve the AI chatbots and help them understand the complicated nuances and undertones of human conversations.

Chatbots are nothing but applications that are used by businesses or other entities to conduct an automatic conversation between a human and an AI. These conversations may be via text or speech. Chatbots are required to understand and mimic human conversation while interacting with humans from all over the world. From the first chatbot to be created ELIZA to Amazon’s ALEXA today, chatbots have come a long way. In this tutorial, we are going to cover all the basics you need to follow along and create a basic chatbot that can understand human interaction and also respond accordingly. We will be using speech recognition APIs and also pre-trained Transformer models.

 

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What is NLP?

Tasks in NLP

The task of interpreting and responding to human speech is filled with a lot of challenges that we have discussed in this article. In fact, it takes humans years to overcome these challenges and learn a new language from scratch. To overcome these challenges, programmers have integrated a lot of functions to the NLP tech to create useful technology that you can use to understand human speech, process, and return a suitable response.

NLP tasks are responsible for breaking down human text and audio signals from voice data in ways that can be analyzed and converted into data that the computer understands. Some of the tasks included in NLP data ingestion are as follows:

  • Speech recognition: speech recognition or speech to text conversion is an incredibly important process involved in speech analysis. Speech tagging or grammatical tagging is a subprocess of speech recognition that allows a computer to break down speech and tag it with implied context, accent or other speech definition points.
  • Word sense Disambiguation: In human speech, a word may have multiple meanings. The process of word sense disambiguation is a semantic analysis that selects the meaning of a given word that best suits it in the given context. For example, this process assists in deciding whether a word is a verb or a pronoun.
  • Named Entity Recognition or NEM: NEM identifies words and phrases as useful entities for example, ‘Dev’ is a person’s name and ‘America’ is the name of a country.

Sentiment analysis: Human speech often contains sentiments and undertones Extracting these undertones and hidden contexts such as attitude, sarcasm, fear or joy is perhaps the most difficult task that is undertaken by NLP processes.

Types of AI Chatbots

Chatbots are a relatively recent concept and despite having a huge number of programs and NLP tools, we basically have just two different categories of chatbots based on the NLP technology that they utilize. These two types of chatbots are as follows:

  • Scripted chatbots: Scripted chatbots are classified as chatbots that work on pre-determined scripts that are created and stored in their library. Whenever a user types a query or speaks a query (in the case of chatbots equipped with speech to text conversion modules), the chatbot responds to this query according to the pre-determined script that is stored within its library.One of the cons of such a chatbot is the fact that user needs to provide their query in a very structured manner with comma-separated commands or other forms of a regular expression that makes it.

      easier for the bot to perform string analysis and understand the query. This makes this kind of chatbot difficult to integrate with NLP aided speech to text        conversion modules. Hence, these chatbots can hardly ever be converted into smart virtual assistants.

  • Artificially Intelligent Chatbots: Artificially intelligent chatbots, as the name suggests, are created to mimic human-like traits and responses. NLP or Natural Language Processing is hugely responsible for enabling such chatbots to understand the dialects and undertones of human conversation. NLP combined with artificial intelligence creates a truly intelligent chatbot that can respond to nuanced questions and learn from every interaction to create better-suited responses the next time.The AI chatbots have been developed to assist human users on different platforms such as automated chat support or virtual assistants helping with a song or restaurant selection.

Challenges For Your AI Chatbot

In the current world, computers are not just machines celebrated for their calculation powers. Today, the need of the hour is interactive and intelligent machines that can be used by all human beings alike. For this, computers need to be able to understand human speech and its differences.

NLP technologies have made it possible for machines to intelligently decipher human text and actually respond to it as well. However, communication amongst humans is not a simple affair. There are a lot of undertones dialects and complicated wording that makes it difficult to create a perfect chatbot or virtual assistant that can understand and respond to every human.

To overcome the problem of chaotic speech, developers have had to face a few key hurdles which need to be explored in order to keep improving these chatbots. To understand these hurdles or problems we need to under how NLP works to convert human speech into something an algorithm or AI understands. Here’s a list of snags that a chatbot hits whenever users are trying to interact with it:
  1. Synonyms, homonyms, slang
  2. Misspellings
  3. Abbreviations
  4. Complex punctuation rules
  5. Accents, dialects and speech differences with the age and other issues of humans. (for eg. lisps, drawls, etc)
To a human brain, all of this seems really simple as we have grown and developed in the presence of all of these speech modulations and rules. However, the process of training an AI chatbot is similar to a human trying to learn an entirely new language from scratch. The different meanings tagged with intonation, context, voice modulation, etc are difficult for a machine or algorithm to process and then respond to. NLP technologies are constantly evolving to create the best tech to help machines understand these differences and nuances better.

What is Speech Recognition?

NLP or Natural Language Processing has a number of subfields as conversation and speech are tough for computers to interpret and respond to. One such subfield of NLP is Speech Recognition. Speech Recognition works with methods and technologies to enable recognition and translation of human spoken languages into something that the computer or AI can understand and respond to. For computers, understanding numbers is easier than understanding words and speech. When the first few speech recognition systems were being created, IBM Shoebox was the first to get decent success with understanding and responding to a select few English words. Today, we have a number of successful examples which understand myriad languages and respond in the correct dialect and language as the human interacting with it. Most of this success is through the SpeechRecognition library.

The Language Model for AI Chatbot

Here, we will use a Transformer Language Model for our chatbot. This model was presented by Google and it replaced the earlier traditional sequence to sequence models with attention mechanisms. This language model dynamically understands speech and its undertones. Hence, the model easily performs NLP tasks. Some of the most popularly used language models are Google’s BERT and OpenAI’s GPT. These models have multidisciplinary functionalities and billions of parameters which helps to improve the chatbot and make it truly intelligent.

This is where the chatbot becomes intelligent and not just a scripted bot that will be ready to handle any test thrown at them. The main package that we will be using in our code here is the Transformers package provided by HuggingFace. This tool is popular amongst developers as it provides tools that are pre-trained and ready to work with a variety of NLP tasks. In the code below, we have specifically used the DialogGPT trained and created by Microsoft based on millions of conversations and ongoing chats on the Reddit platform in a given interval of time.

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