Definition and evaluation of phonetic units for speech recognition by hidden Markov models

This paper describes the design of a phonetic unit set for recognition of continuous speech where each unit is represented by an Hidden Markov Model. Starting from a unit set definition like classical diphones, many variations were made in order to have an improvement in recognition performance and a reduction in storage requirements. The definition of this unit set is presented, along with experimental comparisons with classical diphones and with phoneme-like units. The performance was qualitatively evaluated using the segmentation of the training data-base provided from the Viterbi algorithm in a forced recognition task. Classical recognition experiments have also been carried out using different "difficult vocabularies" as test.