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Binaural unmasking, a key feature of normal binaural hearing, can refer to the improved intelligibility of masked speech by adding masking that facilitates perceived separation of target and masker. A question relevant for cochlear implant users with single-sided deafness (SSD-CI) is whether binaural unmasking can still be achieved if the additional masking is spectrally degraded and shifted. CIs restore some aspects of binaural hearing to these listeners, although binaural unmasking remains limited. Notably, these listeners may experience a mismatch between the frequency information perceived through the CI and that perceived by their normal hearing ear. Employing acoustic simulations of SSD-CI with normal hearing listeners, the present study confirms a previous simulation study that binaural unmasking is severely limited when interaural frequency mismatch between the input frequency range and simulated place of stimulation exceeds 1–2 mm. The present study also shows that binaural unmasking is largely retained when the input frequency range is adjusted to match simulated place of stimulation, even at the expense of removing low-frequency information. This result bears implications for the mechanisms driving the type of binaural unmasking of the present study and for mapping the frequency range of the CI speech processor in SSD-CI users. [ABSTRACT FROM AUTHOR]

Cochlear implant (CI) recipients have difficulty understanding speech in noise even at moderate signal-to-noise ratios. Knowing the mechanisms they use to understand speech in noise may facilitate the search for better speech processing algorithms. In the present study, a computational model is used to assess whether CI users' vowel identification in noise can be explained by formant frequency cues (F1 and F2). Vowel identification was tested with 12 unilateral CI users in quiet and in noise. Formant cues were measured from vowels in each condition, specific to each subject's speech processor. Noise distorted the location of vowels in the F2 vs F1 plane in comparison to quiet. The best fit model to subjects' data in quiet produced model predictions in noise that were within 8% of actual scores on average. Predictions in noise were much better when assuming that subjects used a priori knowledge regarding how formant information is degraded in noise (experiment 1). However, the model's best fit to subjects' confusion matrices in noise was worse than in quiet, suggesting that CI users utilize formant cues to identify vowels in noise, but to a different extent than how they identify vowels in quiet (experiment 2). [ABSTRACT FROM AUTHOR]

Even though speech signals trigger coding in the cochlea to convey speech information to the central auditory structures, little is known about the neural mechanisms involved in such processes. The purpose of this study was to understand the encoding of formant cues and how it relates to vowel recognition in listeners. Neural representations of formants may differ across listeners; however, it was hypothesized that neural patterns could still predict vowel recognition. To test the hypothesis, the frequency-following response (FFR) and vowel recognition were obtained from 38 normal-hearing listeners using four different vowels, allowing direct comparisons between behavioral and neural data in the same individuals. FFR was employed because it provides an objective and physiological measure of neural activity that can reflect formant encoding. A mathematical model was used to describe vowel confusion patterns based on the neural responses to vowel formant cues. The major findings were (1) there were large variations in the accuracy of vowel formant encoding across listeners as indexed by the FFR, (2) these variations were systematically related to vowel recognition performance, and (3) the mathematical model of vowel identification was successful in predicting good vs poor vowel identification performers based exclusively on physiological data. [ABSTRACT FROM AUTHOR]

The multidimensional phoneme identification model is applied to consonant confusion matrices obtained from 28 postlingually deafened cochlear implant users. This model predicts consonant matrices based on these subjects' ability to discriminate a set of postulated spectral, temporal, and amplitude speech cues as presented to them by their device. The model produced confusion matrices that matched many aspects of individual subjects' consonant matrices, including information transfer for the voicing, manner, and place features, despite individual differences in age at implantation, implant experience, device and stimulation strategy used, as well as overall consonant identification level. The model was able to match the general pattern of errors between consonants, but not the full complexity of all consonant errors made by each individual. The present study represents an important first step in developing a model that can be used to test specific hypotheses about the mechanisms cochlear implant users employ to understand speech. [ABSTRACT FROM AUTHOR]

A simple mathematical model is presented that predicts vowel identification by cochlear implant users based on these listeners’ resolving power for the mean locations of first, second, and/or third formant energies along the implanted electrode array. This psychophysically based model provides hypotheses about the mechanism cochlear implant users employ to encode and process the input auditory signal to extract information relevant for identifying steady-state vowels. Using one free parameter, the model predicts most of the patterns of vowel confusions made by users of different cochlear implant devices and stimulation strategies, and who show widely different levels of speech perception (from near chance to near perfect). Furthermore, the model can predict results from the literature, such as Skinner, et al. [(1995). Ann. Otol. Rhinol. Laryngol. 104, 307–311] frequency mapping study, and the general trend in the vowel results of Zeng and Galvin’s [(1999). Ear Hear. 20, 60–74] studies of output electrical dynamic range reduction. The implementation of the model presented here is specific to vowel identification by cochlear implant users, but the framework of the model is more general. Computational models such as the one presented here can be useful for advancing knowledge about speech perception in hearing impaired populations, and for providing a guide for clinical research and clinical practice. [ABSTRACT FROM AUTHOR]

Information transfer analysis [G. A. Miller and P. E. Nicely, J. Acoust. Soc. Am. 27, 338–352 (1955)] is a tool used to measure the extent to which speech features are transmitted to a listener, e.g., duration or formant frequencies for vowels; voicing, place and manner of articulation for consonants. An information transfer of 100% occurs when no confusions arise between phonemes belonging to different feature categories, e.g., between voiced and voiceless consonants. Conversely, an information transfer of 0% occurs when performance is purely random. As asserted by Miller and Nicely, the maximum-likelihood estimate for information transfer is biased to overestimate its true value when the number of stimulus presentations is small. This small-sample bias is examined here for three cases: a model of random performance with pseudorandom data, a data set drawn from Miller and Nicely, and reported data from three studies of speech perception by hearing impaired listeners. The amount of overestimation can be substantial, depending on the number of samples, the size of the confusion matrix analyzed, as well as the manner in which data are partitioned therein. [ABSTRACT FROM AUTHOR]

Even though speech signals trigger coding in the cochlea to convey speech information to the central auditory structures, little is known about the neural mechanisms involved in such processes. The purpose of this study was to understand the encoding of formant cues and how it relates to vowel recognition in listeners. Neural representations of formants may differ across listeners; however, it was hypothesized that neural patterns could still predict vowel recognition. To test the hypothesis, the frequency-following response (FFR) and vowel recognition were obtained from 38 normal-hearing listeners using four different vowels, allowing direct comparisons between behavioral and neural data in the same individuals. FFR was employed because it provides an objective and physiological measure of neural activity that can reflect formant encoding. A mathematical model was used to describe vowel confusion patterns based on the neural responses to vowel formant cues. The major findings were (1) there were large variations in the accuracy of vowel formant encoding across listeners as indexed by the FFR, (2) these variations were systematically related to vowel recognition performance, and (3) the mathematical model of vowel identification was successful in predicting good vs poor vowel identification performers based exclusively on physiological data.