Modeling lexical stress in read and spontaneous speech

Although prosodic information has long been thought important for speech recognition, few demonstrations exist of its effective use in recognition systems. Lexical stress information has been shown to improve recognition performance by allowing the differentiation of confusable words (e.g., Rudnicky and Li, DARPA Workshop on Speech Recogn., June 1988). In this study, lexical stress modeling for a spreadsheet system with significant number of confusable words (e.g., EIGHTY and EIGHTEEN) is examined. The models used here have been evaluated on both read and spontaneous speech. A database of over 400 spreadsheet and numeric utterances was available for training a (HMM‐based) speaker‐independent continuous‐speech system with a 273‐word vocabulary and language perplexity of about 51. Testing data used in this study were based on read utterances and data generated in a separate study examining the use of a spoken‐language spreadsheet. This latter set includes: (a) a “spontaneous” set, composed of parsable utter...