Discrete Parameter Markov Model Speech Recognition Using Soft Decision Quantization
Original Publication Date: 1987-Apr-01
Included in the Prior Art Database: 2005-Feb-01
In a discrete parameter Markov model speech recognition system, a space is partitioned into a plurality of exclusive regions based on specified speech-related characteristics, such as power spectrum features. Each region is represented as a spectral prototype. Input speech is examined at periodic intervals, the speech for each interval representing a frame F. Based on the speech-related characteristics of each frame, an ordered list of the closest M out of the N total prototypes is formed for each frame. Two frames are differentiated if their rank-ordered lists differ; the spectral space will be quantized into N!/(N-M)! regions. Hence, for each transition (i.e., arc) of a Markov model, there must be a probability distribution for N!/(N-M)! possible outcomes.