Index of content:
Volume 111, Issue 4, April 2002
- SPEECH PERCEPTION 
111(2002); http://dx.doi.org/10.1121/1.1458026View Description Hide Description
This article describes a model in which the acoustic speech signal is processed to yield a discrete representation of the speech stream in terms of a sequence of segments, each of which is described by a set (or bundle) of binary distinctive features. These distinctive features specify the phonemic contrasts that are used in the language, such that a change in the value of a feature can potentially generate a new word. This model is a part of a more general model that derives a word sequence from this feature representation, the words being represented in a lexicon by sequences of feature bundles. The processing of the signal proceeds in three steps: (1) Detection of peaks, valleys, and discontinuities in particular frequency ranges of the signal leads to identification of acoustic landmarks. The type of landmark provides evidence for a subset of distinctive features called articulator-free features (e.g., [vowel], [consonant], [continuant]). (2) Acoustic parameters are derived from the signal near the landmarks to provide evidence for the actions of particular articulators, and acoustic cues are extracted by sampling selected attributes of these parameters in these regions. The selection of cues that are extracted depends on the type of landmark and on the environment in which it occurs. (3) The cues obtained in step (2) are combined, taking context into account, to provide estimates of “articulator-bound” features associated with each landmark (e.g., [lips], [high], [nasal]). These articulator-bound features, combined with the articulator-free features in (1), constitute the sequence of feature bundles that forms the output of the model. Examples of cues that are used, and justification for this selection, are given, as well as examples of the process of inferring the underlying features for a segment when there is variability in the signal due to enhancement gestures (recruited by a speaker to make a contrast more salient) or due to overlap of gestures from neighboring segments.
Auditory normalization of French vowels synthesized by an articulatory model simulating growth from birth to adulthood111(2002); http://dx.doi.org/10.1121/1.1459467View Description Hide Description
The present article aims at exploring the invariant parameters involved in the perceptual normalization of French vowels. A set of 490 stimuli, including the ten French vowels /i y u e ø o ɛ œ ɔ a/ produced by an articulatory model, simulating seven growth stages and seven fundamental frequency values, has been submitted as a perceptual identification test to 43 subjects. The results confirm the important effect of the tonality distance between F1 and in perceived height. It does not seem, however, that height perception involves a binary organization determined by the 3–3.5-Bark critical distance. Regarding place of articulation, the tonotopic distance between F1 and F2 appears to be the best predictor of the perceived front–back dimension. Nevertheless, the role of the difference between F2 and F3 remains important. Roundedness is also examined and correlated to the effective second formant, involving spectral integration of higher formants within the 3.5-Bark critical distance. The results shed light on the issue of perceptual invariance, and can be interpreted as perceptual constraints imposed on speech production.
111(2002); http://dx.doi.org/10.1121/1.1456928View Description Hide Description
When listening to languages learned at a later age, speech intelligibility is generally lower than when listening to one’s native language. The main purpose of this study is to quantify speech intelligibility in noise for specific populations of non-native listeners, only broadly addressing the underlying perceptual and linguistic processing. An easy method is sought to extend these quantitative findings to other listener populations. Dutch subjects listening to Germans and English speech, ranging from reasonable to excellent proficiency in these languages, were found to require a 1–7 dB better speech-to-noise ratio to obtain 50% sentence intelligibility than native listeners. Also, the psychometric function for sentence recognition in noise was found to be shallower for non-native than for native listeners (worst-case slope around the 50% point of 7.5%/dB, compared to 12.6%/dB for native listeners). Differences between native and non-native speech intelligibility are largely predicted by linguistic entropy estimates as derived from a letter guessing task. Less effective use of context effects (especially semantic redundancy) explains the reduced speech intelligibility for non-native listeners. While measuring speech intelligibility for many different populations of listeners (languages, linguistic experience) may be prohibitively time consuming, obtaining predictions of non-native intelligibility from linguistic entropy may help to extend the results of this study to other listener populations.