Effects of Noise on the Behavioral and Neural Categorization of Speech.
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ABSTRACT:
SUBMITTER: Bidelman GM
PROVIDER: S-EPMC7057933 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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 a set of auditory-inspired features and feeds them to the neural network to produce an estimation of which frequency channels contain more perceptually important information (higher signal-to-noise ratio, SNR). This estimate is used to attenuate noise-dominated and retain speech-dominated CI channels for electrical stimulation, as in traditional n-of-m CI coding strategies. The proposed algorithm was evaluated by measuring the speech-in-noise performance of 14 CI users using three types of background noise. Two NNSE algorithms were compared: a speaker-dependent algorithm, that was trained on the target speaker used for testing, and a speaker-independent algorithm, that was trained on different speakers. Significant improvements in the intelligibility of speech in stationary and fluctuating noises were found relative to the unprocessed condition for the speaker-dependent algorithm in all noise types and for the speaker-independent algorithm in 2 out of 3 noise types. The NNSE algorithms used noise-specific neural networks that generalized to novel segments of the same noise type and worked over a range of SNRs. The proposed algorithm has the potential to improve the intelligibility of speech in noise for CI users while meeting the requirements of low computational complexity and processing delay for application in CI devices.
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Project description:The speech signal is notoriously variable, with the same phoneme realized differently depending on factors like talker and phonetic context. Variance in the speech signal has led to a proliferation of theories of how listeners recognize speech. A promising approach, supported by computational modeling studies, is contingent categorization, wherein incoming acoustic cues are computed relative to expectations. We tested contingent encoding empirically. Listeners were asked to categorize fricatives in CV syllables constructed by splicing the fricative from one CV syllable with the vowel from another CV syllable. The two spliced syllables always contained the same fricative, providing consistent bottom-up cues; however on some trials, the vowel and/or talker mismatched between these syllables, giving conflicting contextual information. Listeners were less accurate and slower at identifying the fricatives in mismatching splices. This suggests that listeners rely on context information beyond bottom-up acoustic cues during speech perception, providing support for contingent categorization.
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Project description:Healthy aging leads to complex changes in the functional network of speech processing in a noisy environment. The dual-route neural architecture has been applied to the study of speech processing. Although evidence suggests that senescent increases activity in the brain regions across the dorsal and ventral stream regions to offset reduced periphery, the regulatory mechanism of dual-route functional networks underlying such compensation remains largely unknown. Here, by utilizing functional near-infrared spectroscopy (fNIRS), we investigated the compensatory mechanism of the dual-route functional connectivity, and its relationship with healthy aging by using a speech perception task at varying signal-to-noise ratios (SNR) in healthy individuals (young adults, middle-aged adults, and older adults). Results showed that the speech perception scores showed a significant age-related decrease with the reduction of the SNR. The analysis results of dual-route speech processing networks showed that the functional connection of Wernicke's area and homolog Wernicke's area were age-related increases. Further to clarify the age-related characteristics of the dual-route speech processing networks, graph-theoretical network analysis revealed an age-related increase in the efficiency of the networks, and the age-related differences in nodal characteristics were found both in Wernicke's area and homolog Wernicke's area under noise environment. Thus, Wernicke's area might be a key network hub to maintain efficient information transfer across the speech process network with healthy aging. Moreover, older adults would recruit more resources from the homologous Wernicke's area in a noisy environment. The recruitment of the homolog of Wernicke's area might provide a means of compensation for older adults for decoding speech in an adverse listening environment. Together, our results characterized dual-route speech processing networks at varying noise environments and provided new insight for the compensatory theories of how aging modulates the dual-route speech processing functional networks.
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Project description:The development of the ability to categorize speech sounds is often viewed as occurring primarily during infancy via perceptual learning mechanisms. However, a number of studies suggest that even after infancy, children's categories become more categorical and well defined through about age 12. We investigated the cognitive changes that may be responsible for such development using a visual world paradigm experiment based on (McMurray, Tanenhaus, & Aslin, 2002). Children from 3 age groups (7-8, 12-13, and 17-18 years) heard a token from either a b/p or s/? continua spanning 2 words (beach/peach, ship/sip) and selected its referent from a screen containing 4 pictures of potential lexical candidates. Eye movements to each object were monitored as a measure of how strongly children were committing to each candidate as perception unfolds in real-time. Results showed an ongoing sharpening of speech categories through 18, which was particularly apparent during the early stages of real-time perception. When analysis targeted to specifically within-category sensitivity to continuous detail, children exhibited increasingly gradient categories over development, suggesting that increasing sensitivity to fine-grained detail in the signal enables these more discrete categorizations. Together these suggest that speech development is a protracted process in which children's increasing sensitivity to within-category detail in the signal enables increasingly sharp phonetic categories. (PsycINFO Database Record
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Project description:Many individuals with seemingly normal hearing abilities struggle to understand speech in noisy backgrounds. To understand why this might be the case, we investigated the neural representation of speech in the auditory midbrain of gerbils with "hidden hearing loss" through noise exposure that increased hearing thresholds only temporarily. In noise-exposed animals, we observed significantly increased neural responses to speech stimuli, with a more pronounced increase at moderate than at high sound intensities. Noise exposure reduced discriminability of neural responses to speech in background noise at high sound intensities, with impairment most severe for tokens with relatively greater spectral energy in the noise-exposure frequency range (2-4 kHz). At moderate sound intensities, discriminability was surprisingly improved, which was unrelated to spectral content. A model combining damage to high-threshold auditory nerve fibers with increased response gain of central auditory neurons reproduced these effects, demonstrating that a specific combination of peripheral damage and central compensation could explain listening difficulties despite normal hearing thresholds.
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Project description:Evaluation of patients who are unable to provide behavioral responses on standard clinical measures is challenging due to the lack of standard objective (non-behavioral) clinical audiological measures that assess the outcome of an intervention (e.g., hearing aids). Brainstem responses to short consonant-vowel stimuli (speech-auditory brainstem responses [speech-ABRs]) have been proposed as a measure of subcortical encoding of speech, speech detection, and speech-in-noise performance in individuals with normal hearing. Here, we investigated the potential application of speech-ABRs as an objective clinical outcome measure of speech detection, speech-in-noise detection and recognition, and self-reported speech understanding in 98 adults with sensorineural hearing loss. We compared aided and unaided speech-ABRs, and speech-ABRs in quiet and in noise. In addition, we evaluated whether speech-ABR F0 encoding (obtained from the complex cross-correlation with the 40 ms [da] fundamental waveform) predicted aided behavioral speech recognition in noise or aided self-reported speech understanding. Results showed that (a) aided speech-ABRs had earlier peak latencies, larger peak amplitudes, and larger F0 encoding amplitudes compared to unaided speech-ABRs; (b) the addition of background noise resulted in later F0 encoding latencies but did not have an effect on peak latencies and amplitudes or on F0 encoding amplitudes; and (c) speech-ABRs were not a significant predictor of any of the behavioral or self-report measures. These results show that speech-ABR F0 encoding is not a good predictor of speech-in-noise recognition or self-reported speech understanding with hearing aids. However, our results suggest that speech-ABRs may have potential for clinical application as an objective measure of speech detection with hearing aids.
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Project description:Verbal communication in noisy backgrounds is challenging. Understanding speech in background noise that fluctuates in intensity over time is particularly difficult for hearing-impaired listeners with a sensorineural hearing loss (SNHL). The reduction in fast-acting cochlear compression associated with SNHL exaggerates the perceived fluctuations in intensity in amplitude-modulated sounds. SNHL-induced changes in the coding of amplitude-modulated sounds may have a detrimental effect on the ability of SNHL listeners to understand speech in the presence of modulated background noise. To date, direct evidence for a link between magnified envelope coding and deficits in speech identification in modulated noise has been absent. Here, magnetoencephalography was used to quantify the effects of SNHL on phase locking to the temporal envelope of modulated noise (envelope coding) in human auditory cortex. Our results show that SNHL enhances the amplitude of envelope coding in posteromedial auditory cortex, whereas it enhances the fidelity of envelope coding in posteromedial and posterolateral auditory cortex. This dissociation was more evident in the right hemisphere, demonstrating functional lateralization in enhanced envelope coding in SNHL listeners. However, enhanced envelope coding was not perceptually beneficial. Our results also show that both hearing thresholds and, to a lesser extent, magnified cortical envelope coding in left posteromedial auditory cortex predict speech identification in modulated background noise. We propose a framework in which magnified envelope coding in posteromedial auditory cortex disrupts the segregation of speech from background noise, leading to deficits in speech perception in modulated background noise.SIGNIFICANCE STATEMENT People with hearing loss struggle to follow conversations in noisy environments. Background noise that fluctuates in intensity over time poses a particular challenge. Using magnetoencephalography, we demonstrate anatomically distinct cortical representations of modulated noise in normal-hearing and hearing-impaired listeners. This work provides the first link among hearing thresholds, the amplitude of cortical representations of modulated sounds, and the ability to understand speech in modulated background noise. In light of previous work, we propose that magnified cortical representations of modulated sounds disrupt the separation of speech from modulated background noise in auditory cortex.
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Project description:Face masks are an important tool for preventing the spread of COVID-19. However, it is unclear how different types of masks affect speech recognition in different levels of background noise. To address this, we investigated the effects of four masks (a surgical mask, N95 respirator, and two cloth masks) on recognition of spoken sentences in multi-talker babble. In low levels of background noise, masks had little to no effect, with no more than a 5.5% decrease in mean accuracy compared to a no-mask condition. In high levels of noise, mean accuracy was 2.8-18.2% lower than the no-mask condition, but the surgical mask continued to show no significant difference. The results demonstrate that different types of masks generally yield similar accuracy in low levels of background noise, but differences between masks become more apparent in high levels of noise.
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