Prediction of consonant recognition in quiet for listeners with normal and impaired hearing using an auditory model

Age-related reduction of amplitude modulation frequency selectivity

The perception of amplitude modulations (AMs) has been characterized by a frequency-selective process in the temporal envelope domain and simulated in computational auditory processing and perception models using a modulation filterbank. Such AM frequency-selective processing has been argued to be critical for the perception of complex sounds, including speech. This study aimed at investigating the effects of age on behavioral AM frequency selectivity in young (n = 11, 22-29 years) versus older (n = 10, 57-77 years) listeners with normal hearing, using a simultaneous AM masking paradigm with a sinusoidal carrier (2.8 kHz), target modulation frequencies of 4, 16, 64, and 128 Hz, and narrowband-noise modulation maskers. A reduction of AM frequency selectivity by a factor of up to 2 was found in the older listeners. While the observed AM selectivity co-varied with the unmasked AM detection sensitivity, the age-related broadening of the masked threshold patterns remained stable even when AM sensitivity was similar across groups for an extended stimulus duration. The results from the present study might provide a valuable basis for further investigations exploring the effects of age and reduced AM frequency selectivity on complex sound perception as well as the interaction of age and hearing impairment on AM processing and perception.

Speech intelligibility prediction based on modulation frequency-selective processing

Speech intelligibility models can provide insights regarding the auditory processes involved in human speech perception and communication. One successful approach to modelling speech intelligibility has been based on the analysis of the amplitude modulations present in speech as well as competing interferers. This review covers speech intelligibility models that include a modulation-frequency selective processing stage i.e., a modulation filterbank, as part of their front end. The speech-based envelope power spectrum model [sEPSM, Jørgensen and Dau (2011). J. Acoust. Soc. Am. 130(3), 1475-1487], several variants of the sEPSM including modifications with respect to temporal resolution, spectro-temporal processing and binaural processing, as well as the speech-based computational auditory signal processing and perception model [sCASP; Relaño-Iborra et al. J. Acoust. Soc. Am. 146(5), 3306-3317], which is based on an established auditory signal detection and masking model, are discussed. The key processing stages of these models for the prediction of speech intelligibility across a variety of acoustic conditions are addressed in relation to competing modeling approaches. The strengths and weaknesses of the modulation-based analysis are outlined and perspectives presented, particularly in connection with the challenge of predicting the consequences of individual hearing loss on speech intelligibility.

[Characterization of a closed-set logatome test : Documentation of audiometric data: discrimination function and reproducibility]

BACKGROUND

Logatomes, nonsensical combinations of consonants and vowels, are suitable for a precise capture and analysis of individual phonemes as fundamental modules of speech in audiometric diagnostics.

OBJECTIVE

The aim of this prospective study was to capture the audiometric characteristics of a closed-set logatome test. The slope of the discrimination function at the speech reception threshold (SRT) and the reproducibility were analyzed.

MATERIAL AND METHODS

A set of 102 intensity varied and randomized logatomes were presented in the form of consonant-vowel-consonant to 25 hearing unimpaired adults. The measurements were performed in a free field setting and were each repeated after a 2-week interval. The subjects were requested to repeat the heard logatome in a closed response test of 10 items per sound item on a touchscreen.

RESULTS

The slope of the mean discrimination function at the SRT was on average 4%/dB; however, the mean discrimination function slope was steeper for the initial consonant than for the final one. The differences of the test and retest results at the SRT showed a standard deviation of 13% for consonants. These differences were normally distributed. There were no significant differences between test and retest.

CONCLUSION

The slope of the discrimination function at the SRT appeared to be shallow but was comparable to established word tests. Finally, there was no evidence of a learning effect in the retest, which emphasizes the low redundancy of the speech material and makes it an attractive complementary option to routine audiometric diagnostics.

Predicting effects of hearing-instrument signal processing on consonant perception

This study investigated the influence of hearing-aid (HA) and cochlear-implant (CI) processing on consonant perception in normal-hearing (NH) listeners. Measured data were compared to predictions obtained with a speech perception model [Zaar and Dau (2017). J. Acoust. Soc. Am. 141, 1051-1064] that combines an auditory processing front end with a correlation-based template-matching back end. In terms of HA processing, effects of strong nonlinear frequency compression and impulse-noise suppression were measured in 10 NH listeners using consonant-vowel stimuli. Regarding CI processing, the consonant perception data from DiNino et al. [(2016). J. Acoust. Soc. Am. 140, 4404-4418] were considered, which were obtained with noise-vocoded vowel-consonant-vowel stimuli in 12 NH listeners. The inputs to the model were the same stimuli as were used in the corresponding experiments. The model predictions obtained for the two data sets showed a large agreement with the perceptual data both in terms of consonant recognition and confusions, demonstrating the model's sensitivity to supra-threshold effects of hearing-instrument signal processing on consonant perception. The results could be useful for the evaluation of hearing-instrument processing strategies, particularly when combined with simulations of individual hearing impairment.

Predicting consonant recognition and confusions in normal-hearing listeners

The perception of consonants in background noise has been investigated in various studies and was shown to critically depend on fine details in the stimuli. In this study, a microscopic speech perception model is proposed that represents an extension of the auditory signal processing model by Dau, Kollmeier, and Kohlrausch [(1997). J. Acoust. Soc. Am. 102, 2892-2905]. The model was evaluated based on the extensive consonant perception data set provided by Zaar and Dau [(2015). J. Acoust. Soc. Am. 138, 1253-1267], which was obtained with normal-hearing listeners using 15 consonant-vowel combinations mixed with white noise. Accurate predictions of the consonant recognition scores were obtained across a large range of signal-to-noise ratios. Furthermore, the model yielded convincing predictions of the consonant confusion scores, such that the predicted errors were clustered in perceptually plausible confusion groups. The large predictive power of the proposed model suggests that adaptive processes in the auditory preprocessing in combination with a cross-correlation based template-matching back end can account for some of the processes underlying consonant perception in normal-hearing listeners. The proposed model may provide a valuable framework, e.g., for investigating the effects of hearing impairment and hearing-aid signal processing on phoneme recognition.

Forward-Masked Frequency Selectivity Improvements in Simulated and Actual Cochlear Implant Users Using a Preprocessing Algorithm

Frequency selectivity can be quantified using masking paradigms, such as psychophysical tuning curves (PTCs). Normal-hearing (NH) listeners show sharp PTCs that are level- and frequency-dependent, whereas frequency selectivity is strongly reduced in cochlear implant (CI) users. This study aims at (a) assessing individual shapes of PTCs in CI users, (b) comparing these shapes to those of simulated CI listeners (NH listeners hearing through a CI simulation), and (c) increasing the sharpness of PTCs using a biologically inspired dynamic compression algorithm, BioAid, which has been shown to sharpen the PTC shape in hearing-impaired listeners. A three-alternative-forced-choice forward-masking technique was used to assess PTCs in 8 CI users (with their own speech processor) and 11 NH listeners (with and without listening through a vocoder to simulate electric hearing). CI users showed flat PTCs with large interindividual variability in shape, whereas simulated CI listeners had PTCs of the same average flatness, but more homogeneous shapes across listeners. The algorithm BioAid was used to process the stimuli before entering the CI users’ speech processor or the vocoder simulation. This algorithm was able to partially restore frequency selectivity in both groups, particularly in seven out of eight CI users, meaning significantly sharper PTCs than in the unprocessed condition. The results indicate that algorithms can improve the large-scale sharpness of frequency selectivity in some CI users. This finding may be useful for the design of sound coding strategies particularly for situations in which high frequency selectivity is desired, such as for music perception.

Envelope and intensity based prediction of psychoacoustic masking and speech intelligibility

Human auditory perception and speech intelligibility have been successfully described based on the two concepts of spectral masking and amplitude modulation (AM) masking. The power-spectrum model (PSM) [Patterson and Moore (1986). Frequency Selectivity in Hearing, pp. 123-177] accounts for effects of spectral masking and critical bandwidth, while the envelope power-spectrum model (EPSM) [Ewert and Dau (2000). J. Acoust. Soc. Am. 108, 1181-1196] has been successfully applied to AM masking and discrimination. Both models extract the long-term (envelope) power to calculate signal-to-noise ratios (SNR). Recently, the EPSM has been applied to speech intelligibility (SI) considering the short-term envelope SNR on various time scales (multi-resolution speech-based envelope power-spectrum model; mr-sEPSM) to account for SI in fluctuating noise [Jørgensen, Ewert, and Dau (2013). J. Acoust. Soc. Am. 134, 436-446]. Here, a generalized auditory model is suggested combining the classical PSM and the mr-sEPSM to jointly account for psychoacoustics and speech intelligibility. The model was extended to consider the local AM depth in conditions with slowly varying signal levels, and the relative role of long-term and short-term SNR was assessed. The suggested generalized power-spectrum model is shown to account for a large variety of psychoacoustic data and to predict speech intelligibility in various types of background noise.