Evaluation of a spectral subtraction strategy to suppress reverberant energy in cochlear implant devices.

The smearing effects of room reverberation can significantly impair the ability of cochlear implant (CI) listeners to understand speech. To ameliorate the effects of reverberation, current dereverberation algorithms focus on recovering the direct sound from the reverberated signal by inverse filtering the reverberation process. This contribution describes and evaluates a spectral subtraction (SS) strategy capable of suppressing late reflections. Late reflections are the most detrimental to speech intelligibility by CI listeners as reverberation increases. By tackling only the late part of reflections, it is shown that users of CI devices can benefit from the proposed strategy even in highly reverberant rooms. The proposed strategy is also compared against an ideal reverberant (binary) masking approach. Speech intelligibility results indicate that the proposed SS solution is able to suppress additive reverberant energy to a degree comparable to that achieved by an ideal binary mask. The added advantage is that the SS strategy proposed in this work can allow for a potentially real-time implementation in clinical CI processors.

Evaluation of an English language phoneme-based imagined speech brain computer interface with low-cost electroencephalography.

Introduction: Paralyzed and physically impaired patients face communication difficulties, even when they are mentally coherent and aware. Electroencephalographic (EEG) brain-computer interfaces (BCIs) offer a potential communication method for these people without invasive surgery or physical device controls. Methods: Although virtual keyboard protocols are well documented in EEG BCI paradigms, these implementations are visually taxing and fatiguing. All English words combine 44 unique phonemes, each corresponding to a unique EEG pattern. In this study, a complete phoneme-based imagined speech EEG BCI was developed and tested on 16 subjects. Results: Using open-source hardware and software, machine learning models, such as k-nearest neighbor (KNN), reliably achieved a mean accuracy of 97 ± 0.001%, a mean F1 of 0.55 ± 0.01, and a mean AUC-ROC of 0.68 ± 0.002 in a modified one-versus-rest configuration, resulting in an information transfer rate of 304.15 bits per minute. In line with prior literature, the distinguishing feature between phonemes was the gamma power on channels F3 and F7. Discussion: However, adjustments to feature selection, trial window length, and classifier algorithms may improve performance. In summary, these are iterative changes to a viable method directly deployable in current, commercially available systems and software. The development of an intuitive phoneme-based EEG BCI with open-source hardware and software demonstrates the potential ease with which the technology could be deployed in real-world applications.

Effects of low harmonics on tone identification in natural and vocoded speech.

This study investigated the contribution of low-frequency harmonics to identifying Mandarin tones in natural and vocoded speech in quiet and noisy conditions. Results showed that low-frequency harmonics of natural speech led to highly accurate tone identification; however, for vocoded speech, low-frequency harmonics yielded lower tone identification than stimuli with full harmonics, except for tone 4. Analysis of the correlation between tone accuracy and the amplitude-F0 correlation index suggested that "more" speech contents (i.e., more harmonics) did not necessarily yield better tone recognition for vocoded speech, especially when the amplitude contour of the signals did not co-vary with the F0 contour.

The effects of binaural spectral resolution mismatch on Mandarin speech perception in simulated electric hearing.

This study assessed the effects of binaural spectral resolution mismatch on the intelligibility of Mandarin speech in noise using bilateral cochlear implant simulations. Noise-vocoded Mandarin speech, corrupted by speech-shaped noise at 0 and 5 dB signal-to-noise ratios, were presented unilaterally or bilaterally to normal-hearing listeners with mismatched spectral resolution between ears. Significant binaural benefits for Mandarin speech recognition were observed only with matched spectral resolution between ears. In addition, the performance of tone identification was more robust to noise than that of sentence recognition, suggesting factors other than tone identification might account more for the degraded sentence recognition in noise.

The perception of telephone-processed speech by combined electric and acoustic stimulation.

This study assesses the effects of adding low- or high-frequency information to the band-limited telephone-processed speech on bimodal listeners' telephone speech perception in quiet environments. In the proposed experiments, bimodal users were presented under quiet listening conditions with wideband speech (WB), bandpass-filtered telephone speech (300-3,400 Hz, BP), high-pass filtered speech (f > 300 Hz, HP, i.e., distorted frequency components above 3,400 Hz in telephone speech were restored), and low-pass filtered speech (f < 3,400 Hz, LP, i.e., distorted frequency components below 300 Hz in telephone speech were restored). Results indicated that in quiet environments, for all four types of stimuli, listening with both hearing aid (HA) and cochlear implant (CI) was significantly better than listening with CI alone. For both bimodal and CI-alone modes, there were no statistically significant differences between the LP and BP scores and between the WB and HP scores. However, the HP scores were significantly better than the BP scores. In quiet conditions, both CI alone and bimodal listening achieved the largest benefits when telephone speech was augmented with high rather than low-frequency information. These findings provide support for the design of algorithms that would extend higher frequency information, at least in quiet environments.