# Using skewness to perform syllable recognition – Part I (Theory)

Currently the syllable class contains an accumulator for various letters. If these letters exist it corresponds to a special syllable. I however think that this is fairly limited because “fish” and “shift” will be interpreted as the same word. Yet, µSpeech should be capable of better: we are able to tell when an individual letter has been said. For instance the occurrence of “f” in fish should occur more at the beginning, where as the occurrence of “f” in “shift” should occur at the end.

For solving this problem I have been considering two methods: The skewness and a method based on pure calculus. Today I will explore Skewness.

Skewness – The theory

Skewness is a measure of how something leans to one side. Its statistical definition is: $\frac{\mu_3}{\sigma^3}$

Where $\mu_3$ is the 3rd moment of the mean i.e $E[(x-\mu)^3]$.

Now going back to High school mathematics: $E[(x-\mu)^3] = E[x^3 - 3x^2\mu + 3x\mu^2 - \mu^3] = E[x^3] - 3\mu E[x^2] + 3\mu^2 E[x] - \mu^3$

Given that $\mu = E[x]$: $E[(x-\mu)^3] = E[x^3] - 3\mu (E[x^2] - \mu^2) - \mu^3$

Since $\sigma^2 = E[x^2]-\mu^2$: $E[(x-\mu)^3] = E[x^3] - 3\mu \sigma^2 - \mu^3$

This results in the necessity for the algorithm to compute 3 variables: $E[x^3], \mu, \sigma^2$

Making the algorithm online

Now given that µSpeech has stringent memory requirements it seems imperative that we devise the algorithm so that the following can be done:

• Data is not kept in an array.
• Computation is minimal.

Thus keeping these in mind it seems necessary to look at ways of computing the three variables online. To start with lets tackle the simplest algorithm, the one for µ.

Given that $\mu = E[x] = \sum n_i p_i = \sum \frac{x_i}{n}$, one can write an algorithm as such: $\mu_i = \mu_{i-1} + \frac{x_i - \mu_{i-1}}{i}$

This can be extended to $E[x^3]$: $E[x^3]_i = E[x^3]_{i-1} +\frac{x^3_i-E[x^3]_{i-1}}{i}$

The third value which we need to compute $\sigma^2$. $\sigma^2 = E[x^2]-E[x]^2$

So we need to find $E[x^2]$: $E[x^2]_i = E[x^2]_{i-1} +\frac{x^2_i-E[x^2]_{i-1}}{i}$

Part 2 coming soon.