Though a vocabulary of several thousand words will often cover most of a person's typical phrases, recognition systems must have vocabularies approaching one hundred thousand words to adequately cover realistic tasks such as audio indexing of broadcast news. A statistical speech recognition approach has some added benefit that is demonstrated in Figure 5. One significant feature of this approach is that knowledge in the system is organized in a hierarchical fashion. This last level is typically implemented using three-state Hidden Markov models as shown in Figure 5. The next level up, the lexicon, resembles the pronunciation portion of a standard dictionary.
Networks attempt to predict word sequences one word at a time.
They are most often implemented as state machines in which each transition outputs a word. These are extremely popular for command and control applications, such as navigating a sequence of menus on a desktop computer. N-grams, on the other hand, attempt to enumerate common sequences of words. Such language models can be very powerful but require large amounts of memory to process. Another significant feature of the system is that information detected by the signal is actually time-aligned against the speech signal as shown in Figure 5.