Optimizing Speech Recognition Using a Computational Model of Human Hearing: Effect of Noise Type and Efferent Time Constants

Physiological and psychophysical methods allow for an extended investigation of ascending (afferent) neural pathways from the ear to the brain in mammals, and their role in enhancing signals in noise. However, there is increased interest in descending (efferent) neural fibers in the mammalian auditory pathway. This efferent pathway operates via the olivocochlear system, modifying auditory processing by cochlear innervation and enhancing human ability to detect sounds in noisy backgrounds. Effective speech intelligibility may depend on a complex interaction between efferent time-constants and types of background noise. In this study, an auditory model with efferent-inspired processing provided the front-end to an automatic-speech-recognition system (ASR), used as a tool to evaluate speech recognition with changes in time-constants (50 to 2000 ms) and background noise type (unmodulated and modulated noise). With efferent activation, maximal speech recognition improvement (for both noise types) occurred for signal-to-noise ratios around 10 dB, characteristic of real-world speech-listening situations. Net speech improvement due to efferent activation (NSIEA) was smaller in modulated noise than in unmodulated noise. For unmodulated noise, NSIEA increased with increasing time-constant. For modulated noise, NSIEA increased for time-constants up to 200 ms but remained similar for longer time-constants, consistent with speech-envelope modulation times important to speech recognition in modulated noise. The model improves our understanding of the complex interactions involved in speech recognition in noise, and could be used to simulate the difficulties of speech perception in noise as a consequence of different types of hearing loss

A frequency-selective feedback model of auditory efferent suppression and its implications for the recognition of speech in noise

The potential contribution of the peripheral auditory efferent system to our understanding of speech in a background of competing noise was studied using a computer model of the auditory periphery and assessed using an automatic speech recognition system. A previous study had shown that a fixed efferent attenuation applied to all channels of a multi-channel model could improve the recognition of connected digit triplets in noise [G. J. Brown, R. T. Ferry, and R. Meddis, J. Acoust. Soc. Am. 127, 943?954 (2010)]. In the current study an anatomically justified feedback loop was used to automatically regulate separate attenuation values for each auditory channel. This arrangement resulted in a further enhancement of speech recognition over fixed-attenuation conditions. Comparisons between multi-talker babble and pink noise interference conditions suggest that the benefit originates from the model?s ability to modify the amount of suppression in each channel separately according to the spectral shape of the interfering sounds

A computer model of auditory efferent suppression: Implications for the recognition of speech in noise

The neural mechanisms underlying the ability of human listeners to recognize speech in the presence of background noise are still imperfectly understood. However, there is mounting evidence that the medial olivocochlear system plays an important role, via efferents that exert a suppressive effect on the response of the basilar membrane. The current paper presents a computer modeling study that investigates the possible role of this activity on speech intelligibility in noise. A model of auditory efferent processing [ Ferry, R. T., and Meddis, R. (2007). J. Acoust. Soc. Am. 122, 3519?3526 ] is used to provide acoustic features for a statistical automatic speech recognition system, thus allowing the effects of efferent activity on speech intelligibility to be quantified. Performance of the ?basic? model (without efferent activity) on a connected digit recognition task is good when the speech is uncorrupted by noise but falls when noise is present. However, recognition performance is much improved when efferent activity is applied. Furthermore, optimal performance is obtained when the amount of efferent activity is proportional to the noise level. The results obtained are consistent with the suggestion that efferent suppression causes a ?release from adaptation? in the auditory-nerve response to noisy speech, which enhances its intelligibility

A Binaural Cochlear Implant Sound Coding Strategy Inspired by the Contralateral Medial Olivocochlear Reflex

[EN] Objectives: In natural hearing, cochlear mechanical compression is dynamically adjusted via the efferent medial olivocochlear reflex (MOCR). These adjustments probably help understanding speech in noisy environments and are not available to the users of current cochlear implants (CIs). The aims of the present study are to: (1) present a binaural CI sound processing strategy inspired by the control of cochlear compression provided by the contralateral MOCR in natural hearing; and (2) assess the benefits of the new strategy for understanding speech presented in competition with steady noise with a speech-like spectrum in various spatial configurations of the speech and noise sources. Design: Pairs of CI sound processors (one per ear) were constructed to mimic or not mimic the effects of the contralateral MOCR on compression. For the nonmimicking condition (standard strategy or STD), the two processors in a pair functioned similarly to standard clinical processors (i.e., with fixed back-end compression and independently of each other). When configured to mimic the effects of the MOCR (MOC strategy), the two processors communicated with each other and the amount of backend compression in a given frequency channel of each processor in the pair decreased/increased dynamically (so that output levels dropped/ increased) with increases/decreases in the output energy from the corresponding frequency channel in the contralateral processor. Speech reception thresholds in speech-shaped noise were measured for 3 bilateral CI users and 2 single-sided deaf unilateral CI users. Thresholds were compared for the STD and MOC strategies in unilateral and bilateral listening conditions and for three spatial configurations of the speech and noise sources in simulated free-field conditions: speech and noise sources colocated in front of the listener, speech on the left ear with noise in front of the listener, and speech on the left ear with noise on the right ear. In both bilateral and unilateral listening, the electrical stimulus delivered to the test ear(s) was always calculated as if the listeners were wearing bilateral processors. Results: In both unilateral and bilateral listening conditions, mean speech reception thresholds were comparable with the two strategies for colocated speech and noise sources, but were at least 2 dB lower (better) with the MOC than with the STD strategy for spatially separated speech and noise sources. In unilateral listening conditions, mean thresholds improved with increasing the spatial separation between the speech and noise sources regardless of the strategy but the improvement was significantly greater with the MOC strategy. In bilateral listening conditions, thresholds improved significantly with increasing the speech-noise spatial separation only with the MOC strategy. Conclusions: The MOC strategy (1) significantly improved the intelligibility of speech presented in competition with a spatially separated noise source, both in unilateral and bilateral listening conditions; (2) produced significant spatial release from masking in bilateral listening conditions, something that did not occur with fixed compression; and (3) enhanced spatial release from masking in unilateral listening conditions. The MOC strategy as implemented here, or a modified version of it, may be usefully applied in CIs and in hearing aids

Multifaceted evaluation of a binaural cochlear‐ implant sound‐processing strategy inspired by the medial olivocochlear reflex

[ES]El objetivo de esta tesis es evaluar experimentalmente la audición de los usuarios de implantes cocleares con una estrategia de procesamiento binaural de sonidos inspirada en el reflejo olivococlear medial, denominada "estrategia MOC". La tesis describe cuatro estudios dirigidos a comparar la inteligibilidad del habla en ruido, la localización de fuentes sonoras y el esfuerzo auditivo con procesadores de sonido estándar y con diversos procesadores MOC diseñados para reflejar de forma más o menos realista el tiempo de activación del reflejo olivococlear medial natural y sus efectos sobre la comprensión coclear humana

A Comparative Study of Computational Models of Auditory Peripheral System

A deep study about the computational models of the auditory peripheral system from three different research groups: Carney, Meddis and Hemmert, is presented here. The aim is to find out which model fits the data best and which properties of the models are relevant for speech recognition. To get a first approximation, different tests with tones have been performed with seven models. Then we have evaluated the results of these models in the presence of speech. Therefore, two models were studied deeply through an automatic speech recognition (ASR) system, in clean and noisy background and for a diversity of sound levels. The post stimulus time histogram help us to see how the models that improved the offset adaptation present the ¿dead time¿. For its part, the synchronization evaluation for tones and modulated signals, have highlighted the better result from the models with offset adaptation. Finally, tuning curves and Q10dB (added to ASR results) on contrary have indicated that the selectivity is not a property needed for speech recognition. Besides the evaluation of the models with ASR have demonstrated the outperforming of models with offset adaptation and the triviality of using cat or human tuning for speech recognition. With this results, we conclude that mostly the model that better fits the data is the one described by Zilany et al. (2009) and the property unquestionable for speech recognition would be a good offset adaptation that offers a better synchronization and a better ASR result. For ASR system it makes no big difference if offset adaptation comes from a shift of the auditory nerve response or from a power law adaptation in the synapse.Vendrell Llopis, N. (2010). A Comparative Study of Computational Models of Auditory Peripheral System. http://hdl.handle.net/10251/20433.Archivo delegad

Perceptual compensation for reverberation in human listeners and machines

This thesis explores compensation for reverberation in human listeners and machines. Late reverberation is typically understood as a distortion which degrades intelligibility. Recent research, however, shows that late reverberation is not always detrimental to human speech perception. At times, prolonged exposure to reverberation can provide a helpful acoustic context which improves identification of reverberant speech sounds. The physiology underpinning our robustness to reverberation has not yet been elucidated, but is speculated in this thesis to include efferent processes which have previously been shown to improve discrimination of noisy speech. These efferent pathways descend from higher auditory centres, effectively recalibrating the encoding of sound in the cochlea. Moreover, this thesis proposes that efferent-inspired computational models based on psychoacoustic principles may also improve performance for machine listening systems in reverberant environments. A candidate model for perceptual compensation for reverberation is proposed in which efferent suppression derives from the level of reverberation detected in the simulated auditory nerve response. The model simulates human performance in a phoneme-continuum identification task under a range of reverberant conditions, where a synthetically controlled test-word and its surrounding context phrase are independently reverberated. Addressing questions which arose from the model, a series of perceptual experiments used naturally spoken speech materials to investigate aspects of the psychoacoustic mechanism underpinning compensation. These experiments demonstrate a monaural compensation mechanism that is influenced by both the preceding context (which need not be intelligible speech) and by the test-word itself, and which depends on the time-direction of reverberation. Compensation was shown to act rapidly (within a second or so), indicating a monaural mechanism that is likely to be effective in everyday listening. Finally, the implications of these findings for the future development of computational models of auditory perception are considered

Predicting confusions and intelligibility of noisy speech

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (leaves 202-207).Current predictors of speech intelligibility are inadequate for making predictions of speech confusions caused by acoustic interference. This thesis is inspired by the need for a capability to understand and predict speech confusions caused by acoustic interference. The goal of this thesis is to develop models of auditory speech processing capable of predicting phonetic confusions by normally-hearing listeners, under a variety of acoustic distortions. In particular, we focus on modeling the Medial Olivocochlear efferent pathway (which provides feedback from the brain stem to the peripheral auditory system) and demonstrate its potential for speech identification in noise. Our results produced representations and performance that were robust to varying levels of additive noise and which mimicked human performance as measured by the Chi-squared test.by David P. Messing.Ph.D

Methods of Optimizing Speech Enhancement for Hearing Applications

Speech intelligibility in hearing applications suffers from background noise. One of the most effective solutions is to develop speech enhancement algorithms based on the biological traits of the auditory system. In humans, the medial olivocochlear (MOC) reflex, which is an auditory neural feedback loop, increases signal-in-noise detection by suppressing cochlear response to noise. The time constant is one of the key attributes of the MOC reflex as it regulates the variation of suppression over time. Different time constants have been measured in nonhuman mammalian and human auditory systems. Physiological studies reported that the time constant of nonhuman mammalian MOC reflex varies with the properties (e.g. frequency, bandwidth) changes of the stimulation. A human based study suggests that time constant could vary when the bandwidth of the noise is changed. Previous works have developed MOC reflex models and successfully demonstrated the benefits of simulating the MOC reflex for speech-in-noise recognition. However, they often used fixed time constants. The effect of the different time constants on speech perception remains unclear. The main objectives of the present study are (1) to study the effect of the MOC reflex time constant on speech perception in different noise conditions; (2) to develop a speech enhancement algorithm with dynamic time constant optimization to adapt to varying noise conditions for improving speech intelligibility. The first part of this thesis studies the effect of the MOC reflex time constants on speech-in-noise perception. Conventional studies do not consider the relationship between the time constants and speech perception as it is difficult to measure the speech intelligibility changes due to varying time constants in human subjects. We use a model to investigate the relationship by incorporating Meddis’ peripheral auditory model (which includes a MOC reflex) with an automatic speech recognition (ASR) system. The effect of the MOC reflex time constant is studied by adjusting the time constant parameter of the model and testing the speech recognition accuracy of the ASR. Different time constants derived from human data are evaluated in both speech-like and non-speech like noise at the SNR levels from -10 dB to 20 dB and clean speech condition. The results show that the long time constants (≥1000 ms) provide a greater improvement of speech recognition accuracy at SNR levels≤10 dB. Maximum accuracy improvement of 40% (compared to no MOC condition) is shown in pink noise at the SNR of 10 dB. Short time constants (<1000 ms) show recognition accuracy over 5% higher than the longer ones at SNR levels ≥15 dB. The second part of the thesis develops a novel speech enhancement algorithm based on the MOC reflex with a time constant that is dynamically optimized, according to a lookup table for varying SNRs. The main contributions of this part include: (1) So far, the existing SNR estimation methods are challenged in cases of low SNR, nonstationary noise, and computational complexity. High computational complexity would increase processing delay that causes intelligibility degradation. A variance of spectral entropy (VSE) based SNR estimation method is developed as entropy based features have been shown to be more robust in the cases of low SNR and nonstationary noise. The SNR is estimated according to the estimated VSE-SNR relationship functions by measuring VSE of noisy speech. Our proposed method has an accuracy of 5 dB higher than other methods especially in the babble noise with fewer talkers (2 talkers) and low SNR levels (< 0 dB), with averaging processing time only about 30% of the noise power estimation based method. The proposed SNR estimation method is further improved by implementing a nonlinear filter-bank. The compression of the nonlinear filter-bank is shown to increase the stability of the relationship functions. As a result, the accuracy is improved by up to 2 dB in all types of tested noise. (2) A modification of Meddis’ MOC reflex model with a time constant dynamically optimized against varying SNRs is developed. The model incudes simulated inner hair cell response to reduce the model complexity, and now includes the SNR estimation method. Previous MOC reflex models often have fixed time constants that do not adapt to varying noise conditions, whilst our modified MOC reflex model has a time constant dynamically optimized according to the estimated SNRs. The results show a speech recognition accuracy of 8 % higher than the model using a fixed time constant of 2000 ms in different types of noise. (3) A speech enhancement algorithm is developed based on the modified MOC reflex model and implemented in an existing hearing aid system. The performance is evaluated by measuring the objective speech intelligibility metric of processed noisy speech. In different types of noise, the proposed algorithm increases intelligibility at least 20% in comparison to unprocessed noisy speech at SNRs between 0 dB and 20 dB, and over 15 % in comparison to processed noisy speech using the original MOC based algorithm in the hearing aid