Expanding Vocabulary Capabilities of µspeech

In order to take into account the varying complexities of vocabulary used we looked at offline algorithms for skewness. Now I propose a simpler method. Skewness requires a very large number of multiplications and divisions to be performed. This makes it unsuitable for quick calculations on an AVR as the AVR architecture used by an arduino does not contain a multiply instruction, so multiplication is implemented by software not hardware. In order to speed the process up, I came up with another simple yet effective (but not reliable) measure of skewness.

We treat every utterance (syllable) as a probability distribution of individual phonemes. To test the order of which phoneme comes first we record the number of phonemes per unit time. So every 16 cycles we sample the number of phonemes, and from that one keeps a catalog of statistical features

Skewness can be looked at graphically:

Figure 1. Skewness of a unimodal distribution

As one can see the mode of a negatively skewed distribution is to the right of the center of the distribution. Thus the mode – median is positive. This would be opposite for a positively skewed graph. So µSpeech uses this property to determine the order of a sound in an utterance.