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Speech Recognition with HMMs

 

Because speech recognition has been the most common application of HMMs, we will discuss here some of the issues it involves, although this discussion is relevant to many other applications. The basic speech recognition problem can be stated as follows: given a sequence of acoustic descriptors (obtained by pre-processing the speech signal, e.g., spectral information represented by a vector of between approximately 10 and 40 numbers, obtained at a rate of around 10 millisecond per time step), find the sequence of words intended by the speaker who pronounced those words. Training data for solving this task consists of a large set of labeled sequences, i.e., pairs , where is a word sequence and is an acoustic sequence, is an element from a finite set of words, and is a real vector.

  
Figure 5: Example of application of HMMs to isolated word recognition. There is a single HMM for each word in the vocabulary (represented by an oval in the figure), here left-to-right with skips. The recognizer picks the word w with the highest probability , given the acoustic sequence .





Yoshua Bengio
Tue Oct 7 08:34:36 EDT 1997