1. A speech recognition system, comprising: a speech recognition module for using variable-parameter hidden Markov models (VPHMMs) to recognize speech based on extracted speech features and at least one environmental conditioning parameter; and a store in computer memory for storing at least one shared set of parameters that represents a class of piecewise functions for use by the VPHMMs, each of the VPHMMs using a plurality of mixture components L in modeling state observations, and a particular mixture component l of the plurality of mixture components L having at least one mixture component parameter that varies as a function of said at least one environmental conditioning parameter, as approximated by using a piecewise function that includes a plurality of connected segments, the particular mixture component l having a mean parameter μ and a variance parameter Σ that are functions of said at least one environmental conditioning parameter ζ, as represented by: μ i , l , d ⁡ ( ζ r , t , d ) = μ i , l , d ( 0 ) + ξ ⁡ ( ζ r , t , d ❘ μ i , l , d ( 1 ) , … ⁢ , μ i , l , d ( K ) ) , and ∑ i . l . d ⁢ ( ζ r , t , d ) = ∑ i , l , d ( 0 ) ⁢ ξ - 2 ⁡ ( ζ r , t , d ❘ ∑ i , l , d ) ( 1 ) , … ⁢ , ∑ i . l . d ( K ) ) , where i identifies a state, d identifies a dimension, ζ refers to said at least one environmental conditioning parameter, r refers to utterance, t refers to frame, ξ refers to a spline function which serves as the piecewise function, μ i,l,d (0) and Σ i,l,d (0) are Gaussian-component-specific mean and variance parts, and μ i,l,d (1) . . . , μ i,l,d (K) and Σ i,l,d (1) . . . , Σ i,l,d (K) are spline knots that are shareable across different mixture components, the particular mixture component l making reference to said at least one shared set of parameters in the store to constitute the piecewise function.
1. A speech recognition system, comprising: a speech recognition module for using variable-parameter hidden Markov models (VPHMMs) to recognize speech based on extracted speech features and at least one environmental conditioning parameter; and a store in computer memory for storing at least one shared set of parameters that represents a class of piecewise functions for use by the VPHMMs, each of the VPHMMs using a plurality of mixture components L in modeling state observations, and a particular mixture component l of the plurality of mixture components L having at least one mixture component parameter that varies as a function of said at least one environmental conditioning parameter, as approximated by using a piecewise function that includes a plurality of connected segments, the particular mixture component l having a mean parameter μ and a variance parameter Σ that are functions of said at least one environmental conditioning parameter ζ, as represented by: μ i , l , d ⁡ ( ζ r , t , d ) = μ i , l , d ( 0 ) + ξ ⁡ ( ζ r , t , d ❘ μ i , l , d ( 1 ) , … ⁢ , μ i , l , d ( K ) ) , and ∑ i . l . d ⁢ ( ζ r , t , d ) = ∑ i , l , d ( 0 ) ⁢ ξ - 2 ⁡ ( ζ r , t , d ❘ ∑ i , l , d ) ( 1 ) , … ⁢ , ∑ i . l . d ( K ) ) , where i identifies a state, d identifies a dimension, ζ refers to said at least one environmental conditioning parameter, r refers to utterance, t refers to frame, ξ refers to a spline function which serves as the piecewise function, μ i,l,d (0) and Σ i,l,d (0) are Gaussian-component-specific mean and variance parts, and μ i,l,d (1) . . . , μ i,l,d (K) and Σ i,l,d (1) . . . , Σ i,l,d (K) are spline knots that are shareable across different mixture components, the particular mixture component l making reference to said at least one shared set of parameters in the store to constitute the piecewise function. 6. The speech recognition system of claim 1 , wherein the piecewise function for the particular mixture component l is represented in a form in which a Gaussian-specific part is separated from a shared part.