Different Timescales for the Neural Coding of Consonant and Vowel Sounds

Perez, Claudia; Jakkamsetti, Vikram; Carraway, Ryan S.; Perry, Matthew; Kilgard, Michael P.

doi:10.1093/cercor/bhs045

Cited by 64 publications

(134 citation statements)

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“…While the activation patterns were complex for both stimuli, they were sparsely overlapping and the local patterns were distributed across many regions of the auditory cortex. Perez et al (2013) found that different coding strategies were used for vowels and consonants. They found vowel discrimination corresponded largely to neural spike counts in certain brain regions, while consonant discrimination correlated to neural spike timing and not to counts if timing information was eliminated.…”

Section: Ockham's Razor and Levels Of Analysismentioning

confidence: 99%

A unified account of categorical effects in phonetic perception

2016

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Categorical effects are found across speech sound categories, with the degree of these effects ranging from extremely strong categorical perception in consonants to nearly continuous perception in vowels. We show that both strong and weak categorical effects can be captured by a unified model. We treat speech perception as a statistical inference problem, assuming that listeners use their knowledge of categories as well as the acoustics of the signal to infer the intended productions of the speaker. Simulations show that the model provides close fits to empirical data, unifying past findings of categorical effects in consonants and vowels and capturing differences in the degree of categorical effects through a single parameter.

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Section: Ockham's Razor and Levels Of Analysismentioning

confidence: 99%

A unified account of categorical effects in phonetic perception

2016

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“…where rats were trained to discriminate consonants and vowels) and neural recording in the IC and in the auditory cortex, studies have shown that that the brain represents different sounds (e.g. consonants and vowels) at different points in time (Perez et al, 2013). The time lapse between responses in the IC and in the auditory cortex supports the notion that consonant and vowel sounds are represented by neural activity patterns occurring on different timescales (Poeppel, 2003;Carreiras and Price, 2008), and suggests that voice processing operates through the gradual transformation from low-level acoustic information to high-level cognitive category.…”

Section: Inferior Colliculusmentioning

confidence: 99%

Subcortical processing in auditory communication

2015

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a b s t r a c tThe voice is a rich source of information, which the human brain has evolved to decode and interpret. Empirical observations have shown that the human auditory system is especially sensitive to the human voice, and that activity within the voice-sensitive regions of the primary and secondary auditory cortex is modulated by the emotional quality of the vocal signal, and may therefore subserve, with frontal regions, the cognitive ability to correctly identify the speaker's affective state. So far, the network involved in the processing of vocal affect has been mainly characterised at the cortical level. However, anatomical and functional evidence suggests that acoustic information relevant to the affective quality of the auditory signal might be processed prior to the auditory cortex.Here we review the animal and human literature on the main subcortical structures along the auditory pathway, and propose a model whereby the distinction between different types of vocal affect in auditory communication begins at very early stages of auditory processing, and relies on the analysis of individual acoustic features of the sound signal. We further suggest that this early feature-based decoding occurs at a subcortical level along the ascending auditory pathway, and provides a preliminary coarse (but fast) characterisation of the affective quality of the auditory signal before the more refined (but slower) cortical processing is completed.

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“…A peristimulus time histogram (PSTH) classifier (Foffani and Moxon 2004;Engineer et al 2008;Perez et al 2012) was used to calculate the neural discrimination between speech stimuli in units of percent correct. The classifier randomly picked five sites (each having 20 sweeps) at a time and made a template from 19 sweeps (the sweep being considered was excluded) for each speech sound.…”

Section: Neural Recordings and Neural Data Analysismentioning

confidence: 99%

Neural Mechanisms Supporting Robust Discrimination of Spectrally and Temporally Degraded Speech

Ranasinghe

Vrana

Matney

et al. 2012

JARO

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Cochlear implants provide good speech discrimination ability despite highly limited amount of information they transmit compared with normal cochlea. Noise vocoded speech, simulating cochlear implants in normal hearing listeners, have demonstrated that spectrally and temporally degraded speech contains sufficient cues to provide accurate speech discrimination. We hypothesized that neural activity patterns generated in the primary auditory cortex by spectrally and temporally degraded speech sounds will account for the robust behavioral discrimination of speech. We examined the behavioral discrimination of noise vocoded consonants and vowels by rats and recorded neural activity patterns from rat primary auditory cortex (A1) for the same sounds. We report the first evidence of behavioral discrimination of degraded speech sounds by an animal model. Our results show that rats are able to accurately discriminate both consonant and vowel sounds even after significant spectral and temporal degradation. The degree of degradation that rats can tolerate is comparable to human listeners. We observed that neural discrimination based on spatiotemporal patterns (spike timing) of A1 neurons is highly correlated with behavioral discrimination of consonants and that neural discrimination based on spatial activity patterns (spike count) of A1 neurons is highly correlated with behavioral discrimination of vowels. The results of the current study indicate that speech discrimination is resistant to degradation as long as the degraded sounds generate distinct patterns of neural activity.

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Different Timescales for the Neural Coding of Consonant and Vowel Sounds

Cited by 64 publications

References 93 publications

A unified account of categorical effects in phonetic perception

A unified account of categorical effects in phonetic perception

Subcortical processing in auditory communication

Neural Mechanisms Supporting Robust Discrimination of Spectrally and Temporally Degraded Speech

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