Tobias Goehring

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• PhD, Dipl.-Ing.
• MRC Fellow at University of Cambridge

Digits-in-Noise (DIN) tests have been widely applied to assess hearing function and speech-in-noise perception, but their robustness to variations in digit stimuli between listener groups remains unknown. We developed a DIN test for research and clinical applications and evaluated it across different listener groups, including users of cochlear imp...

Speech perception remains challenging for cochlear-implant recipients in conditions containing background noise. Modifications to the sound processing strategy could improve the transmission of information by the cochlear implant, alleviating speech perception difficulties in noise. One such modification, the temporal integrator processing strategy...

Purpose For some cochlear implants (CIs), it is possible to focus electrical stimulation by partially returning current from the active electrode to nearby, intra-cochlear electrodes (partial tripolar (pTP) stimulation). Another method achieves the opposite: “blurring” by stimulating multiple electrodes simultaneously. The Panoramic ECAP (PECAP) me...

During continuous speech perception, endogenous neural activity becomes time-locked to acoustic stimulus features, such as the speech amplitude envelope. This speech–brain coupling can be decoded using non-invasive brain imaging techniques, including electroencephalography (EEG). Neural decoding may provide clinical use as an objective measure of s...

Understanding speech in noisy environments is a challenging task, especially in communication situations with several competing speakers. Despite their ongoing improvement, assistive listening devices and speech processing approaches still do not perform well enough in noisy multi-talker environments, as they may fail to restore the intelligibility...

For cochlear implant (CI) listeners, holding a conversation in noisy and reverberant environments is often challenging. Deep-learning algorithms can potentially mitigate these difficulties by enhancing speech in everyday listening environments. This study compared several deep-learning algorithms with access to one, two unilateral, or six bilateral...

Many people with hearing loss struggle to understand speech in noisy environments, making noise robustness critical for hearing-assistive devices. Recently developed haptic hearing aids, which convert audio to vibration, can improve speech-in-noise performance for cochlear implant (CI) users and assist those unable to access hearing-assistive devic...

Purpose: For some cochlear implants (CIs), it is possible to focus electrical stimulation by partially returning current from the active electrode to adjacent, intra-cochlear electrodes (partial tripolar (pTP) stimulation). Another method achieves the opposite: “blurring” by stimulating multiple electrodes simultaneously. The Panoramic ECAP Method...

For cochlear implant (CI) listeners, holding a conversation in noisy and reverberant environments is often challenging. Deep learning algorithms can potentially mitigate these difficulties by enhancing speech in everyday listening environments. This study compared several deep learning algorithms with access to one, two unilateral or six bilateral...

Many hearing-impaired people struggle to understand speech in background noise, making noise robustness critical for hearing-assistive devices. Recently developed haptic hearing aids, which convert audio to vibration, can improve speech-in-noise performance for cochlear implant (CI) users and assist those unable to access hearing-assistive devices....

During speech listening, recurring patterns of neural activity become temporally coupled to stimulus features, such as the speech envelope. This cortical tracking can be measured using electroencephalography (EEG). Quantifying speech-brain coupling (e.g., as a correlation coefficient) sheds light on the neuro-biological processes underlying percept...

Following speech in noisy and reverberant situations is difficult for cochlear implant (CI) users. This study investigates single-and multi-microphone deep neural network (DNN) speech enhancement algorithms on the joint task of denoising and dereverberation. The DNN algorithms were trained and tested on simulated sound scenes from behind-the-ear he...

This study investigates the information captured by speaker embeddings with relevance to human speech perception. A Convolutional Neural Network was trained to perform one-shot speaker verification under clean and noisy conditions , such that high-level abstractions of speaker-specific features were encoded in a latent embedding vector. We demonstr...

The spectro-temporal ripple for investigating processor effectiveness (STRIPES) test is a psychophysical measure of spectro-temporal resolution in cochlear-implant (CI) listeners. It has been validated using direct-line input and loudspeaker presentation with listeners of the Advanced Bionics CI. This article investigates the suitability of an onli...

Objectives: Electrically evoked compound action-potentials (ECAPs) can be recorded using the electrodes in a cochlear implant (CI) and represent the synchronous responses of the electrically stimulated auditory nerve. ECAPs can be obtained using a forward-masking method that measures the neural response to a probe and masker electrode separately a...

Goal: Advances in computational models of biological systems and artificial neural networks enable rapid virtual prototyping of neuroprosthetics, accelerating innovation in the field. Here, we present an end-to-end computational model for predicting speech perception with cochlear implants (CI), the most widely-used neuroprosthetic. Methods: The...

Millions of people around the world have difficulty hearing. Hearing aids and cochlear implants help people hear better, especially in quiet places. Unfortunately, these devices do not always help in noisy situations like busy classrooms or restaurants. This means that a person with hearing loss may struggle to follow a conversation with friends or...

Objectives: Electrically-Evoked Compound Action-Potentials (ECAPs) can be recorded using the electrodes in a cochlear implant (CI) and represent the synchronous responses of the electrically-stimulated auditory-nerve. ECAPs can be obtained using a forward-masking method that measures the neural response to a probe and masker electrode separately an...

Cochlear implants (CIs) are the world’s most successful sensory prosthesis and have been the subject of intense research and development in recent decades. We critically review the progress in CI research, and its success in improving patient outcomes, from the turn of the century to the present day. The review focuses on the processing, stimulatio...

Cochlear implants (CIs) are the world’s most successful sensory prosthesis and have been the subject of intense research and development in recent decades. We critically review the progress in CI research, and its success in improving patient outcomes, from the turn of the century to the present day. The introduction of directional microphones and...

Cochlear implants (CIs) are neuroprostheses that partially restore hearing for people with severe-to-profound hearing loss. While CIs can provide good speech perception in quiet listening situations for many, they fail to do so in environments with interfering sounds for most listeners. Previous research suggests that this is due to detrimental int...

The knowledge of patient-specific neural excitation patterns from cochlear implants (CIs) can provide important information for optimizing efficacy and improving speech perception outcomes. The Panoramic ECAP ('PECAP') method (Cosentino et al. 2015) uses forward-masked electrically evoked compound action-potentials (ECAPs) to estimate neural activa...

Cochlear implants use electrical stimulation of the auditory nerve to restore the sensation of hearing to deaf people. Unfortunately, the stimulation current spreads extensively within the cochlea, resulting in “blurring” of the signal, and hearing that is far from normal. Current spread can be indirectly measured using the implant electrodes for b...

The STRIPES (Spectro-Temporal Ripple for Investigating Processor EffectivenesS) test is a psychophysical test of spectro-temporal resolution developed for cochlear-implant (CI) listeners. Previously, the test has been strictly controlled to minimize the introduction of extraneous, nonspectro-temporal cues. Here, the effect of relaxing many of those...

The knowledge of patient-specific neural excitation patterns from cochlear implants can provide important information for optimising efficacy and improving speech perception outcomes. The Panoramic ECAP (or ‘PECAP’) method (Cosentino, et al., 2015) uses forward-masked electrically evoked compound action potentials (ECAPs) to estimate neural activat...

Cochlear implant (CI) listeners struggle to understand speech in background noise. Interactions between electrode channels due to current spread increase the masking of speech by noise and lead to difficulties with speech perception. Strategies that reduce channel interaction therefore have the potential to improve speech-in-noise perception by CI...

Speech recognition in noisy environments remains a challenge for cochlear implant (CI) recipients. Unwanted charge interactions between current pulses, both within and between electrode channels, are likely to impair performance. Here we investigate the effect of reducing the number of current pulses on speech perception. This was achieved by imple...

Speech recognition in noisy environments remains a challenge for cochlear implant (CI) recipients. Unwanted charge interactions between current pulses in the same and across different electrode channels are likely to impair performance. Here we investigate the effect of reducing the number of current pulses on speech perception. This was achieved b...

The STRIPES (Spectro-Temporal Ripple for Investigating Processor EffectivenesS) test is a psychophysical test of spectro-temporal resolution developed for cochlear implant (CI) listeners. Previously, the test has been strictly controlled to minimize the introduction of extraneous, non-spectro-temporal cues. Here, the effect of relaxing many of thos...

Cochlear implant (CI) listeners struggle to understand speech in background noise. Interactions between electrode channels due to current spread increase the masking of speech by noise and lead to difficulties with speech perception. Strategies that reduce channel interaction therefore have the potential to improve speech-in-noise perception by CI...

Cochlear implant (CI) listeners struggle to understand speech in background noise. Interactions between electrode channels due to current spread increase the masking of speech by noise and reduce the effective number of channels a CI provides. Therefore, strategies to reduce channel interaction have the potential to improve speech-in-noise percepti...

Cochlear implant (CI) users receive only limited sound information through their implant, which means that they struggle to understand speech in noisy environments. Recent work has suggested that combining the electrical signal from the CI with a haptic signal that provides crucial missing sound information (“electro-haptic stimulation”; EHS) could...

Speech-in-noise perception is a major problem for users of cochlear implants (CIs), especially with non-stationary background noise. Noise-reduction algorithms have produced benefits but relied on a priori information about the target speaker and/or background noise. A recurrent neural network (RNN) algorithm was developed for enhancing speech in n...

Thresholds of asymmetric pulses presented to cochlear implant (CI) listeners depend on polarity in a way that differs across subjects and electrodes. It has been suggested that lower thresholds for cathodic-dominant compared to anodic-dominant pulses reflect good local neural health. We evaluated the hypothesis that this polarity effect (PE) can be...

Speech-in-noise perception is a major problem for users of cochlear implants (CIs), especially with non-stationary background noise such as competing talkers or traffic. Algorithms that facilitate speech perception by attenuating background noise have produced benefits but relied on a priori information about the target speaker and/or background no...

Thresholds of asymmetric pulses presented to cochlear implant (CI) listeners depend on polarity in a way that differs across subjects and electrodes. It has been suggested that lower thresholds for cathodic-dominant compared to anodic-dominant pulses reflect good local neural survival. We evaluated the hypothesis that this polarity effect (PE) can...

The effects on speech intelligibility and sound quality of two noise-reduction algorithms were compared: a deep recurrent neural network (RNN) and spectral subtraction (SS). The RNN was trained using sentences spoken by a large number of talkers with a variety of accents, presented in babble. Different talkers were used for testing. Participants wi...

Cochlear implant (CI) users receive only limited sound information through their implant, which means that they struggle to understand speech in noisy environments. Recent work has suggested that combining the electrical signal from the CI with a haptic signal that provides crucial missing sound information (“electro-haptic stimulation”; EHS) could...

Many cochlear implant (CI) users achieve excellent speech understanding in acoustically quiet conditions, but most perform poorly in the presence of background noise. An important contributor to this poor speech-in-noise performance is the limited transmission of low-frequency sound information through CIs. Recent work has suggested that tactile pr...

Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using recordings of the output of the two microphones of a behind-the-ear hearing aid in response to male...

Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using recordings of the output of the two microphones of a behind-the-ear hearing aid in response to male...

Objective: Processing delay is one of the important factors that limit the development of novel algorithms for hearing devices. In this study, both normal-hearing listeners and listeners with hearing loss were tested for their tolerance of processing delay up to 50 ms using a real-time setup for own-voice and external-voice conditions based on lin...

Machine-learning based approaches to speech enhancement have recently shown great promise for improving speech intelligibility for hearing-impaired listeners. Here, the performance of three machine-learning algorithms and one classical algorithm, Wiener filtering, was compared. Two algorithms based on neural networks were examined, one using a prev...

Speech understanding in adverse acoustic environments is still a major problem for users of hearing-instruments. Recent studies on supervised speech segregation show good promise to alleviate this problem by separating speech-dominated from noise-dominated spectro-temporal regions with estimated time-frequency masks. The current study compared a pr...

Hearing loss can lead to problems with communication, affect the psychological wellbeing and decrease the quality of life of an affected person. One of the main challenges for people with hearing loss is speech perception in noisy environments. Whereas hearing devices such as hearing aids and cochlear implants successfully provide high levels of sp...

Speech understanding in noisy environments is still one of the major challenges for cochlear implant (CI) users in everyday life. We evaluated a speech enhancement algorithm based on neural networks (NNSE) for improving speech intelligibility in noise for CI users. The algorithm decomposes the noisy speech signal into time-frequency units, extracts...

Speech understanding in adverse acoustic environments is still a major problem for users of hearinginstruments. Recent studies on supervised speech segregation show good promise to alleviate this problem by separating speech-dominated from noise-dominated spectro-temporal regions with estimated time-frequency masks. The current study compared a pre...

Traditionally, algorithms that attempt to significantly improve speech intelligibility in noise for cochlear implant (CI) users have met with limited success, especially in the presence of a fluctuating masker. Motivated by previous intelligibility studies of speech synthesized using the ideal binary mask, we propose a framework that integrates a m...

We propose and introduce a testing method for subjective speech quality evaluation of hands-free telephony systems in car environments. In evaluating car hands-free speech quality it is desired to have a procedure available which is repeatable and reproducible, significant and convincing, yet fast and especially resource effective. Proposed is a me...