Prosodic and accentual information for automatic speech recognition

Various aspects relating to the human production and perception of speech have gradually been incorporated into automatic speech recognition systems. Nevertheless, the set of speech prosodic features has not yet been used in an explicit way in the recognition process itself. This study presents an analysis of prosodyʹs three most important parameters, namely energy, fundamental frequency and duration, together with a method for incorporating this information into automatic speech recognition. On the basis of a preliminary analysis, a design is proposed for a prosodic feature classifier in which these parameters are associated with orthographic accentuation. Prosodicaccentual features are incorporated in a hidden Markov model recognizer; their theoretical formulation and experimental setup are then presented. Several experiments were conducted to show how the method performs with a Spanish continuous-speech database. Using this approach to process other database subsets, we obtained a word recognition error reduction rate of 28.91%.