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Introduction to Artificial Intelligence & Expert Systems

Notes text”, or just “STT”. Some SR systems use “training” where an individual speaker reads sections
of text into the SR system. These systems analyze the person’s specific voice and use it to fine
tune the recognition of that person’s speech, resulting in more accurate transcription. Systems
that do not use training are called “Speaker Independent” systems. Systems that use training are
called “Speaker Dependent” systems. Speech recognition applications include voice user interfaces
such as voice dialing (e.g. “Call home”), call routing (e.g. “I would like to make a collect call”),
demotic appliance control, search (e.g. find a podcast where particular words were spoken),
simple data entry (e.g., entering a credit card number), preparation of structured documents
(e.g. a radiology report), speech-to-text processing (e.g., word processors or emails), and aircraft
(usually termed Direct Voice Input). The term “voice recognition” refers to finding the identity
of “who” is speaking, rather than what they are saying. Recognizing the speaker can simplify
the task of translating speech in systems that have been trained on specific person’s voices or it
can be used to authenticate or verify the identity of a speaker as part of a security process.

Self Assessment

State whether the following statements are true or false:
6. Natural Language Processing (NLP) gives machines the ability to read and understand the
languages that humans speak.
7. Unsupervised learning includes both classification and numerical regression.
8. The Air Operations Division uses AI for the rule based expert systems.
9. Voice Recognition (VR) is also known as “automatic speech recognition” or “speech to
text”.

1.4 AI and Related Fields

The AI may be defined as a branch of Computer Science that is concerned with automation of
intelligent behavior. Its principles include the data structures used and knowledge representation,
the algorithms needed to apply the knowledge, and language and programming techniques
used in their implementation.

1.4.1 Common Techniques Used in AI

Neural Networks: Neural networks are structures that can be “trained” to recognize patterns in
inputs. Neural networks typically take a vector of input values and produce a vector of output
values. Inside, they train weights of “neurons”. Neural networks use supervised learning, in
which inputs and outputs are known and the goal is to build a representation of a function that
will approximate the input to output mapping.
They are a way to implement function approximation: given y = f(x ), y = f(x ), …, y = f(x ),
1 1 2 2 n n
construct a function f’ that approximates f. The approximate function f’ is typically smooth: for x’
close to x, we will expect that f’(x’) is close to f’(x). Function approximation serves two purposes:
1. Size: The representation of the approximate function can be significantly smaller than the
true function.
2. Generalization: The approximate function can be used on inputs for which we do not
know the value of the function.
In path finding, the function is f(start, goal) = path. We do not already know the output paths. We
could compute them in some way, perhaps by using A*. But if we are able to compute a path
given (start, goal), then we already know the function f, so why bother approximating it?

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