This study investigated the cues for consonant recognition that are available in the time-intensity envelope of speech. Twelve normal-hearing subjects listened to three sets of spectrally identical noise stimuli created by multiplying noise with the speech envelopes of 19(aCa) natural-speech nonsense syllables. The speech envelope for each of the three noise conditions was derived using a different low-pass filter cutoff (20, 200, and 2000 Hz). Average consonant identification performance was above chance for the three noise conditions and improved significantly with the increase in envelope bandwidth from 20-200 Hz. SINDSCAL multidimensional scaling analysis of the consonant confusions data identified three speech envelope features that divided the 19 consonants into four envelope feature groups ("envemes"). The enveme groups in combination with visually distinctive speech feature groupings ("visemes") can distinguish most of the 19 consonants. These results suggest that near-perfect consonant identification performance could be attained by subjects who receive only enveme and viseme information and no spectral information.

The ability to distinguish electrical stimulation of different electrodes on the basis of "pitch or sharpness" was evaluated with an electrode ranking procedure in 14 individual users of the Nucleus cochlear implant. Prior to the electrode ranking test, absolute thresholds and maximum comfortable loudness levels were measured, and loudness balancing was accomplished across all usable electrodes. Performance on the electrode ranking task was defined in terms of d' per mm of distance between comparison electrodes. Large individual differences were found among cochlear-implant users. In subjects with good to excellent place-pitch sensitivity, the electrode ranking task was limited by a ceiling effect; however, in those with poor to moderate sensitivity d'/mm was relatively constant with spatial separation between electrodes. Place pitch was typically ordered from apical to basal electrodes, i.e., basal electrodes were judged to be higher in pitch than more apical electrodes. However, instances of reversals in place-pitch ordering were seen on some electrodes in some subjects. Instances were also seen of better electrode ranking in the apical half of the electrode array than in the basal half, and vice-versa. Analyses of the electrode ranking functions in terms of d' per stimulus indicated that, in some subjects, perfect performance was reached with as little as 0.75 mm between comparison electrodes, the minimum possible. In other subjects, perfect performance was not reached until the spatial separation between comparison electrodes was over 13 mm, more than three quarters of the entire length of the electrode array. Ten of the subjects also participated in a closed-set recognition task of intervocalic consonants. Although the maximum transmitted information for place of consonant articulation (which is based primarily on spectral speech cues) was only 34%, correlations between place-pitch sensitivity and transmitted speech information were as high as 0.71. This was surprising considering the excellent place-pitch sensitivity exhibited by some of the subjects, and may reflect limitations of the Nucleus speech coding strategy for representing spectrally coded speech information. The two prelingual subjects performed notably poorer on the speech task than the postlingual subjects, even though one of the prelingual subjects demonstrated very good place-pitch sensitivity.

In common practice, hearing aids are fitted by a clinician who measures an audiogram and uses it to generate prescriptive gain and output targets. This report describes an alternative method where users select their own signal processing parameters using an interface consisting of two wheels that optimally map to simultaneous control of gain and compression in each frequency band. The real-world performance of this approach was evaluated via a take-home field trial. Participants with hearing loss were fitted using clinical best practices (audiogram, fit to target, real-ear verification, and subsequent fine tuning). Then, in their everyday lives over the course of a month, participants either selected their own parameters using this new interface (Self group; n ¼ 38) or used the parameters selected by the clinician with limited control (Audiologist Best Practices Group; n ¼ 37). On average, the gain selected by the Self group was within 1.8 dB overall and 5.6 dB per band of that selected by the audiologist. Participants in the Self group reported better sound quality than did those in the Audiologist Best Practices group. In blind sound quality comparisons conducted in the field, participants in the Self group slightly preferred the parameters they selected over those selected by the clinician. Finally, there were no differences between groups in terms of standard clinical measures of hearing aid benefit or speech perception in noise. Overall, the results indicate that it is possible for users to select effective amplification parameters by themselves using a simple interface that maps to key hearing aid signal processing parameters.

Modern hearing aids permit adjustment of a number of electroacoustic parameters, among them frequency response, saturation sound pressure level, and various aspects of compression. Relatively little is known, however, about how the electroacoustic characteristics of hearing aids affect the information-bearing properties of speech. Even less is known about how hearing aids might alleviate or exacerbate the effects of impaired hearing. This article reviews current knowledge in three areas: (a) characteristics of mild/moderate hearing loss, (b) informationbearing aspects of speech, and (c) the relation between electroacoustic characteristics of hearing aids and the speech signal. Concluding suggestions are made regarding the implications of the current data for selecting hearing-aid characteristics.

1) These findings support Bilger's (1984) unifying assumptions that speech recognition is a single construct; therefore, scores on all speech recognition tests must be related and scores on one speech recognition test should be predictive of scores on other tests. 2) Advantages of phoneme scoring include: A) It increases the sample size of scored items for a given list of words, thereby reducing variability in test results. B) Statistical equivalence of phoneme scores for the same 30 phonemes in each of two isophonemic word lists can be evaluated quickly and easily by applying the binomial distribution model to the scores (Thornton & Raffin, 1978). C) Phoneme scores are reasonably accurate predictors of recognition of words in the contextually correct but generally low probability sentences used in this study.