Run-Length Adjustment of Hidden Markov Model Parameters for Speech Recognition
Original Publication Date: 1987-Apr-01
Included in the Prior Art Database: 2005-Feb-01
In a Markov model speech recognition system, the probability of each output symbol (or label or feneme) y which can be generated at a model transition (or arc) is adjusted based on the number of times the symbol occurs in a row (a) in actual data and (b) in synthetic data. In speech recognition, speech may be characterized as a spectral space partitioned into a finite number of regions based on prescribed speech features. Each region is identified by a symbol (or a label or feneme). Incoming speech is examined at successive time intervals based on the prescribed speech features. The symbol which is "closest" (by some measure) to the speech features of an interval is assigned to the interval. The incoming speech can thereby be represented as a sequence of symbols, L1 ...