Jillian

The virtual assistant (VA) is now everywhere in our daily life. “It is a software agent that can perform tasks or services for an individual based on commands or questions” (Wikipedia). The well-known Vas- Alexa, Siri, Cortana, etc.- have different focuses. This week I will be concentrating on Amazon Lex’s chatbots function and attempting to de-blackbox it.

“Amazon Lex is a service for building conversational interfaces into any application using voice and text, which powers the Amazon Alexa virtual assistant” (Wikipedia). According to Amazon Web Service (AWS), “Amazon Lex is a service for building conversational interfaces into any application using voice and text.” It offers advanced deep learning functionalities of automatic speech recognition (ASR) to convert speech to text and natural language understanding (NLU) to identify text to build applications with highly immersive user interfaces and life-like conversational interactions. It usually works with other programs to form a well-functional application architecture like Echo and Alexa.

As it says above, Lex involves in ASR & NLU. For the speech (ASR) part, users speak to the software via an audio feed, and the computer will accordingly create a wave file of words, which will be cleaned by removing background noise and normalizing volume. The filtered waveform will be broken down into small parts- phonemes. Each phoneme is like a chain link and by analyzing them in sequence. The ASR algorithm (RNN we learned last week is an ASR algorithm) uses statistical probability analysis from the first phoneme to deduce whole words and then, from there, complete sentences. When the program knows the sentence, it will provide reasonable responses to users based on its dataset. For the text (NLU) part, still focusing on RNN- the encoder-decoder architecture, users input words or sentences, which will be converted to numeric values-vectors by the algorithm so that the computer will understand. Again, when the program knows the sentence, it will provide reasonable responses to users based on its dataset. The dataset could be set by developers through supervised learning and be accomplished by unsupervised learning.

I recommend checking this workshop case related to Amazon lex collaborating with other APIs (https://github.com/aws-samples/amazon-lex-customerservice-workshop). This is an easy implementation of AWS’s modules.