Perceptual compensation for the effects of reverberation on consonant identification: Evidence from studies with monaural stimuli

Mounting evidence suggests that listeners perceptually compensate for the adverse effects of reverberation in rooms when listening to speech monaurally. However, it is not clear whether the underlying perceptual mechanism would be at all effective in the high levels of stimulus uncertainty that are present in everyday listening. Three experiments investigated monaural compensation with a consonant identification task in which listeners heard different speech on each trial. Consonant confusions frequently arose when a greater degree of reverberation was added to a test-word than to its surrounding context, but compensation became apparent in conditions where the context reverberation was increased to match that of the test-word; here, the confusions were largely resolved. A second experiment shows that information from the test-word itself can also effect compensation. Finally, the time course of compensation was examined by applying reverberation to a portion of the preceding context; consonant identification improves as this portion increases in duration. These findings indicate a monaural compensation mechanism that is likely to be effective in everyday listening, allowing listeners to recalibrate as their reverberant environment changes

Automatic assessment of English learner pronunciation using discriminative classifiers

This paper presents a novel system for automatic assessment of pronunciation quality of English learner speech, based on deep neural network (DNN) features and phoneme specific discriminative classifiers. DNNs trained on a large corpus of native and non-native learner speech are used to extract phoneme posterior probabilities. A part of the corpus includes per phone teacher annotations, which allows training of two Gaussian Mixture Models (GMM), representing correct pronunciations and typical error patterns. The likelihood ratio is then obtained for each observed phone. Several models were evaluated on a large corpus of English-learning students, with a variety of skill levels, and aged 13 upwards. The cross-correlation of the best system and average human annotator reference scores is 0.72, with miss and false alarm rate around 19%. Automatic assessment is 81.6% correct with a high degree of confidence. The new approach significantly outperforms spectral distance based baseline systems

Groundwork for a resource in computational hearing for extended string techniques

Extended techniques and signal processing devices are in-creasingly common in contemporary music composition and performance.At present, few machine listening methods deal reliably with extendedtechniques. Moreover, existing instrumental corpora have not tradition-ally included sources of variation that arise naturally in every-day per-formance environments. In the current study, timbral descriptors areextracted for a range of instrumental techniques, and their dispersion isquantified in order to examine the variation stemming from recordingstrategy choice and performer repetition

Charged particle multiplicity distributions in Z0 hadronic decays

This paper presents an analysis of the multiplicity distributions of charged particles produced in Z0 hadronic decays in the DELPHI detector. It is based on a sample of 25364 events. The average multiplicity is <nch>=20.71±0.04(stat)±0.77(syst) and the dispersion D=6.28±0.03(stat)±0.43(syst). The data are compared with the results at lower energies and with the predictions of phenomenological models. The Lund parton shower model describes the data reasonably well. The multiplicity distributions show approximate KNO-scaling. They also show positive forward-backward correlations that are strongest in the central region of rapidity and for particles of opposite charge. © 1991 Springer-Verlag