4. The speech recognition system of claim 3 , wherein the varied phoneme sequence satisfies a condition that maximizes a multiplication of a prior probability and a conditional probability and is obtained by following Equation 2, Equation 2 being derived from Equation 1 by using Bayes' rule, T * = ⁢ arg ⁢ ⁢ max T ⁢ P ( T ⁢  X ) = ⁢ arg ⁢ ⁢ max T ⁢ P ( X ⁢  T ) ⁢ P ⁡ ( T ) P ⁡ ( X ) ≈ ⁢ arg ⁢ ⁢ max T ⁢ P ( X ⁢  T ) ⁢ P ⁡ ( T ) . Equation ⁢ ⁢ 2
4. The speech recognition system of claim 3 , wherein the varied phoneme sequence satisfies a condition that maximizes a multiplication of a prior probability and a conditional probability and is obtained by following Equation 2, Equation 2 being derived from Equation 1 by using Bayes' rule, T * = ⁢ arg ⁢ ⁢ max T ⁢ P ( T ⁢  X ) = ⁢ arg ⁢ ⁢ max T ⁢ P ( X ⁢  T ) ⁢ P ⁡ ( T ) P ⁡ ( X ) ≈ ⁢ arg ⁢ ⁢ max T ⁢ P ( X ⁢  T ) ⁢ P ⁡ ( T ) . Equation ⁢ ⁢ 2 5. The speech recognition system of claim 4 , wherein the varied phoneme sequence is corrected so that deletion errors in the varied phoneme sequence are removed by following Equation 3, Equation 3 being derived from Equation 2 by applying a phoneme duration model to Equation 2, T * = arg ⁢ ⁢ max T ⁢ P ( X ⁢  T ) ⁢ P ⁡ ( T ) = arg ⁢ ⁢ max T ⁢ P ( X ⁢  S , T ) ⁢ P ( S ⁢  T ) ⁢ P ⁡ ( T ) Equation ⁢ ⁢ 3 wherein P(S|T) is a phoneme duration model serving as a probability that a phoneme will occupy an input frame, which is modeled as a function of a duration and a frequency variation.