transformation linear predictive coding lpc and or cepstral analysis may be employed to obtain the parameters such techniques are described in fundamentals of speech recognition l r rabiner and b h juang prentice hall 1993 the subject matter of which is incorporated herein by reference the set of parameters referred to as a 34 feature vector 34 is computed from a frame of speech data defined by windowing a certain number of samples of the signal each frame represents an observation typically the frame rate is less than the window width i e overlapping frames in order to avoid aliasing speech recognition systems 100 work by attaching numeric scores to possible transcriptions of unlabeled utterances in conventional algorithms for automatic speech recognition such as those disclosed in u s patent application ser no 08 861 413 34 unsupervised hmm adaptation based on speech silence discrimination 34 filed may 21 1997 the subject matter of which is incorporated herein by reference feature vectors that have been extracted by the system contribute equally to the scores however the feature extraction unit 220 extracts feature vectors that contribute by an amount proportional to their arc length methods from differential geometry for example may be used to compute the arc lengths as discussed below typically approximately 10 20 basic features are included along with their first and second derivatives accordingly the input voice signal is transformed into a sequence of feature vectors constituting e g an observation sequence o o 1 o 2 o n having 34 n 34 number of feature vectors fig3 is a detailed functional block diagram of decoder 120 which transforms the sequence of feature vectors in the form of arc lengths received from the preprocessor 110 into a sequence of speech units as shown decoder 120.