Robustness of Acoustic Landmarks in Spontaneously-Spoken American English

ABSTRACT Acoustic landmarks (abrupt changes associated with consonant closures and releases, vowels and glides) play an important role in some models of lexical access (e.g. Stevens 1998, 2002), so it is important to determine how often they actually survive the rigors of articulatory overlap and weakening in spontaneous speech production. A corpus of spontaneous American English speech was collected from 8 adult female speakers and hand labeled for the occurrence of landmarks. Preliminary results for one conversation (240 secs., 610 words, analysis completed for 1003 of 2750 predicted landmarks) show that 86% of landmarks were realized overall, with a sharply lower rate for coronal stops /t/ and /d/. These results suggest that the majority of landmarks are available for detection both by human listeners and automatic recognition algorithms. Ongoing analyses are comparing the rate of automatic detection of these acoustic events with the hand labels, and tabulating the relatively limited set of contexts in which predicted landmarks are lost or changed. Keywords: landmarks, distinctive features, lexical access, feature cues, articulatory overlap. 1. INTRODUCTION Acoustic speech signals contain regions of abrupt change caused by actions of the articulatory tract, such as consonant closures and releases. These regions, which can be called landmarks, play an important role in many models of speech processing. For example, Stevens (1998, 2002) proposes a distinctive-feature-based model of human speech processing in which landmarks play several critical roles. First, they provide information about a particularly important class of distinctive features: the articulator-free features (Halle 1990, Stevens and Keyser to appear), which correspond roughly to the manner features. By specifying the nature and serial order of the closures and releases for consonants, and the extremum landmarks for glides and vowels, the landmarks allow the listener to formulate an initial representation of the CV segmental structure of the utterance or phrase based on very early processing of the incoming signal. Second, this preliminary estimate of the articulator-free features, e.g. [consonant], [sonorant], [continuant], [strident], [vowel] and [glide], places strong constraints on the set of remaining features that must be recognized for each segment. For example, if the landmark is a vowel landmark, no analysis for acoustic cues to the feature [strident] need be carried out in that region. Thus landmark detection constrains the type of further information that the processor must look for in the signal, by specifying the articulator-free features. Third, the landmark string specifies locations where the signal is particularly rich in information about those additional features i.e. about the articulator-bound features of voicing and place. This knowledge facilitates efficient further processing for feature cues, making it unnecessary to compute values for every parameter at every location. Finally, the initial CV representation that the listener forms on the basis of the landmarks, although incomplete, can serve as the organizing framework for the listenerOs processing of other aspects of the utterance, such as words, phrasal groupings and prominence patterns. This may provide information (e.g. about lexical stress) that can help to constrain lexical access processing, and in addition may allow some kinds of higher-level prosodic processing to begin before a complete representation of the features, segments and words of the utterance has been formulated. Because landmarks play such a critical role in this feature-cue-based processing model, it is important to know how often they are present in the signal in order to evaluate the model. This is a particularly significant issue for informal continuous speech,