The speech intelligibility index (SII) model was modified to allow individualized parameters. These parameters included the relative weights of speech cues in five octave-frequency bands ranging from 0.25 to 4 kHz, i.e., the band importance function, and the transfer function that allows the SII to generate predictions on speech-recognition scores. A Bayesian adaptive procedure, the quick-band-importance-function (qBIF) procedure, was utilized to enable efficient estimation of the SII parameters from individual listeners. In two experiments, the SII parameters were estimated for 30 normal-hearing adults using Institute of Electrical and Electronics Engineers (IEEE) sentences at speech levels of 55, 65, and 75 dB sound pressure level (in Experiment I) and for 15 hearing-impaired (HI) adult listeners using amplified IEEE or AzBio sentences (in Experiment II). In both experiments, even without prior training, the estimated model parameters showed satisfactory reliability between two runs of the qBIF procedure at least one week apart. For the HI listeners, inter-listener variability in most estimated SII parameters was larger than intra-listener variability of the qBIF procedure.

Purpose: The signal-to-noise ratio (SNR) for speech presented in background noise may vary after being processed by digital hearing aids with nonlinear signal processing algorithms, such as wide dynamic range compression (WDRC). A phase inversion technique has been previously developed to assess the output SNR of hearing aids. However, systematic validations of this technique have not been conducted. This study aims to validate the phase inversion technique. Method: A simulated hearing aid with multichannel WDRC was implemented, from which the output SNRs, computed via shadow filtering, for connected speech in background noise were directly computed. The agreement between the shadow filter output SNRs and those estimated using the phase inversion technique for the same stimuli was utilized to validate the phase inversion technique. The background noise was 2- or 20-talker babble noise, and the speech stimuli were presented at SNRs of −10 to +10 dB at the input of the simulated hearing aid. The simulated hearing aid was configured to provide amplification for four representative audiograms, and the WDRC was set to be fast or slow acting. To investigate the effects of additive noise, independent of the presented noise stimulus, on the phase inversion estimated output SNR, the same simulated hearing aid was implemented with an additive Gaussian noise at its input (45 and 60 dB SPL). Results: Results showed that the phase inversion technique could either overestimate or underestimate output SNR, depending on the test condition; the estimation errors tended to coincide with temporal landmarks, such as natural pauses between consecutive sentences or fricatives; and increasing the simulated noise led to poorer estimates of output SNR. Conclusions: Results imply that the accuracy of the phase inversion technique is dependent on the test conditions. Thus, the phase inversion technique should be used with caution, and its validity should be evaluated further.

Because smartphones are ubiquitous in modern society, they can serve as excellent mobile tools for students to explore aspects of acoustic measurements in and outside of the classroom. In this talk, I will present a set of lab activities based on smartphones which have allowed students studying speech, language and hearing sciences to use their smartphones as a sound level meter and waveform/spectrum analyzer to measure sounds in their daily lives. In group work, students can compare results from apps installed on different cell phone platforms with measures from a calibrated sound level meter, thereby giving them an opportunity to explore the strengths and limitations of cell phones as measurement devices. This presentation will also discuss how lab reports and classroom discussion can foster the development of analytical skills pertaining to sound analysis.

Personal hearing devices, such as hearing aids, may be fine-tuned for individual users’ preferences by allowing them to self-adjust the amplification profiles. The purpose of the current study was to compare two self-adjustment methods in terms of their test-retest reliability. Both methods estimated preferred amplification profiles in six octave-frequency bands using the method of adjustment. In one method (method A), listeners adjusted the gain in one of six frequency bands using a programmable knob on a given trial; while in the other method (method B), listeners adjusted the gains in all six bands simultaneously according to a linear model using the same programmable knob. Ten normal-hearing listeners participated in the study. The experiment was completed in two test sessions, at least one week apart. During each session, the preferred amplification profile was estimated using both methods. Running speech in quiet or in speech-shaped noise was used as the test stimuli. At the beginning of each method, the initial amplification profile was generated randomly with the gains drawn from a uniform distribution spanning between -25 and 25 dB. The test-retest reliability for method B was better than method A. For method B, the test-retest reliability was better at lower signal-to-noise ratios.