Firstly, this is not because you have two words to identify that you need $N = 2$ states. Your goal is not to train a model with two states - one for each word to recognize - but to train 2 models, one for each word to recognize, and each of these models will have as many states as necessary. In fact, each state in your HMM should correspond to a distinct "stage" in the pronunciation of a word - and will very likely correspond to a phoneme. Your vocabulary size (here, two: "stop" and "go") is external to this. For "stop", there are 4 phonemes. For "go", there are 2 phonemes. So you train a 4-state left-to-right model on the "stop" data; and independently of this, a 2-state left-to-right model on the "go" data. To recognize a word given its MFCCs, you evaluate which of these two models has the highest likelihood given the data. If you had to recognize words within a lexicon of 10 words, you would similarly train 10 HMMs, one for each word, each of these models having a number of states suitable to the length/complexity of the word to recognize.
You need to step back and ask yourself "why HMMs in the first place?". We need HMM for speech recognition because words are made of a sequence of distinct elements in sequence (phonemes). If we want to describe/recognize the word "stop", we need to learn a description which is expressive enough to capture that "first it sounds like ssss, for a short while, then it is tttt for a short while, then it is oooo for a longer amount of time, then it is pppp for a short moment". HMMs are a good match for expressing that - states are phonemes, the transition matrix (which will be here diagonal + upper diagonal) indicates that we move through the word from first phoneme to last phoneme, staying a variable amount of time in each phoneme, and the distribution associated with each state indicates how each phoneme translates into your acoustic features.
It seems also that you are mixing up discrete HMM (in which the observations are drawn from a discrete distribution associated with each state) with continuous HMMs (in which the observations are scalars or vectors, characterized by a continuous distribution such as a gaussian). So the parameter $M$, number of distinct observation symbols, is irrelevant in your case, since your observations are 13-dimensional vectors, an uncountable set! ($M$ would be... the cardinality of the continuum).
I am afraid the introduction material you have picked is not directly relevant to speech recognition - though it is useful for applications in which HMMs are used to recover hidden structure from a discrete observations (and there are many of them, for example parsing/tagging in NLP). Try to master this material without thinking much about your speech recognition problem, and then move on to material about continuous HMMs with multivariate normal distributions - and finally to continuous HMM with mixtures of multivariate normal distributions (since this is what is likely to work best for speech).