1. An article of manufacture comprising a tangible computer readable storage medium including computer usable program code for enhancing performance of a natural language system employing an initial speech recognition model and an initial action classification model, said system, during operation, engaging in a plurality of transactions, said an article of manufacture including: computer usable program code for obtaining, during the operation of said system based on the initial speech recognition model and the initial action classification model, for pertinent ones of said transactions, transaction data comprising at least audio data and associated transaction classification data; computer usable program code for modifying at least one of the initial speech recognition model and the initial action classification model to obtain an iterated speech recognition model and an iterated action classification model, said modifying being based at least in part on said audio data and associated transaction classification data, so as to effect a desired change in an unisolated performance metric pertinent to performance of said natural language system, said modifying comprising jointly modifying the initial speech recognition model and the initial action classification model so as to effect said desired change in said unisolated performance metric; and computer usable program code for repeating said modifying step, with said iterated speech recognition model and said iterated action classification model substituted for the initial speech recognition model and the initial action classification model, until a desired value of said unisolated performance metric is achieved, whereby said unisolated performance metric is recursively optimized; wherein: the initial speech recognition model comprises an initial acoustic model and an initial language model; said iterated speech recognition model comprises an iterated acoustic model and an iterated language model; said modifying of the initial speech recognition model comprises modifying at least one of the initial language model and the initial acoustic model; said unisolated performance metric is given by the equation: R ⁡ ( Λ , θ , Φ ) = ∏ i = 1 M ⁢ P ⁡ ( C * i | A i , Λ , θ , Φ ) , where: Λ is said acoustic model, θ is said language model, Φ is said classification model, M is a total number of training sentences, is a correct class label for sentence i, and A is a speech signal associated with sentence i; said desired value of said unisolated performance metric comprises a maximized value of R; said classification model is constructed in accordance with: C ~ = arg ⁢ ⁢ max C k ⁢ ⁢ P ⁡ ( C k ❘ A ) ≈ arg ⁢ ⁢ max C k ⁢ ∑ n ⁢ ⁢ P ⁡ ( C k ❘ W n ) ⁢ P ⁡ ( W n ❘ A ) ; where W n is n th best word sequence hypothesis; P ⁡ ( W n ❘ A i ) = P θ 0 ⁡ ( W n , A i ) α ⁢ ∑ W ∈ N i ⁢ P θ 0 ⁡ ( W , A i ) α ; α is a log-likelihood scale; further comprising computer usable program code for carrying out a line search on a held-out segment of said transaction data to obtain a value of α.