Neural overlap in processing music and speech

Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing.     

Sustained neural rhythms reveal endogenous oscillations supporting speech perception

Rhythmic sensory or electrical stimulation will produce rhythmic brain responses. These rhythmic responses are often interpreted as endogenous neural oscillations aligned (or “entrained”) to the stimulus rhythm. However, stimulus-aligned brain responses can also be explained as a sequence of evoked responses, which only appear regular due to the rhythmicity of the stimulus, without necessarily involving underlying neural oscillations. To distinguish evoked responses from true oscillatory activity, we tested whether rhythmic stimulation produces oscillatory responses which continue after the end of the stimulus. Such sustained effects provide evidence for true involvement of neural oscillations. In Experiment 1, we found that rhythmic intelligible, but not unintelligible speech produces oscillatory responses in magnetoencephalography (MEG) which outlast the stimulus at parietal sensors. In Experiment 2, we found that transcranial alternating current stimulation (tACS) leads to rhythmic fluctuations in speech perception outcomes after the end of electrical stimulation. We further report that the phase relation between electroencephalography (EEG) responses and rhythmic intelligible speech can predict the tACS phase that leads to most accurate speech perception. Together, we provide fundamental results for several lines of research—including neural entrainment and tACS—and reveal endogenous neural oscillations as a key underlying principle for speech perception.

Just as a child on a swing continues to move after the pushing stops, this study reveals similar entrained rhythmic echoes in brain activity after hearing speech and electrical brain stimulation; perturbation with tACS shows that these brain oscillations help listeners to understand speech.     

The intelligibility of noise-vocoded speech: spectral information available from across-channel comparison of amplitude envelopes

Noise-vocoded (NV) speech is often regarded as conveying phonetic information primarily through temporal-envelope cues rather than spectral cues. However, listeners may infer the formant frequencies in the vocal-tract output-a key source of phonetic detail-from across-band differences in amplitude when speech is processed through a small number of channels. The potential utility of this spectral information was assessed for NV speech created by filtering sentences into six frequency bands, and using the amplitude envelope of each band (≤30 Hz) to modulate a matched noise-band carrier (N). Bands were paired, corresponding to F1 (≈N1 + N2), F2 (≈N3 + N4) and the higher formants (F3' ≈ N5 + N6), such that the frequency contour of each formant was implied by variations in relative amplitude between bands within the corresponding pair. Three-formant analogues (F0 = 150 Hz) of the NV stimuli were synthesized using frame-by-frame reconstruction of the frequency and amplitude of each formant. These analogues were less intelligible than the NV stimuli or analogues created using contours extracted from spectrograms of the original sentences, but more intelligible than when the frequency contours were replaced with constant (mean) values. Across-band comparisons of amplitude envelopes in NV speech can provide phonetically important information about the frequency contours of the underlying formants.     

Abstract coding of audiovisual speech: beyond sensory representation

Is there a neural representation of speech that transcends its sensory properties? Using fMRI, we investigated whether there are brain areas where neural activity during observation of sublexical audiovisual input corresponds to a listener's speech percept (what is "heard") independent of the sensory properties of the input. A target audiovisual stimulus was preceded by stimuli that (1) shared the target's auditory features (auditory overlap), (2) shared the target's visual features (visual overlap), or (3) shared neither the target's auditory or visual features but were perceived as the target (perceptual overlap). In two left-hemisphere regions (pars opercularis, planum polare), the target invoked less activity when it was preceded by the perceptually overlapping stimulus than when preceded by stimuli that shared one of its sensory components. This pattern of neural facilitation indicates that these regions code sublexical speech at an abstract level corresponding to that of the speech percept.     

Musical melody and speech intonation: singing a different tune

Music and speech are often cited as characteristically human forms of communication. Both share the features of hierarchical structure, complex sound systems, and sensorimotor sequencing demands, and both are used to convey and influence emotions, among other functions [1]. Both music and speech also prominently use acoustical frequency modulations, perceived as variations in pitch, as part of their communicative repertoire. Given these similarities, and the fact that pitch perception and production involve the same peripheral transduction system (cochlea) and the same production mechanism (vocal tract), it might be natural to assume that pitch processing in speech and music would also depend on the same underlying cognitive and neural mechanisms. In this essay we argue that the processing of pitch information differs significantly for speech and music; specifically, we suggest that there are two pitch-related processing systems, one for more coarse-grained, approximate analysis and one for more fine-grained accurate representation, and that the latter is unique to music. More broadly, this dissociation offers clues about the interface between sensory and motor systems, and highlights the idea that multiple processing streams are a ubiquitous feature of neuro-cognitive architectures.     

Neural specializations for speech and pitch: moving beyond the dichotomies

The idea that speech processing relies on unique, encapsulated, domain-specific mechanisms has been around for some time. Another well-known idea, often espoused as being in opposition to the first proposal, is that processing of speech sounds entails general-purpose neural mechanisms sensitive to the acoustic features that are present in speech. Here, we suggest that these dichotomous views need not be mutually exclusive. Specifically, there is now extensive evidence that spectral and temporal acoustical properties predict the relative specialization of right and left auditory cortices, and that this is a parsimonious way to account not only for the processing of speech sounds, but also for non-speech sounds such as musical tones. We also point out that there is equally compelling evidence that neural responses elicited by speech sounds can differ depending on more abstract, linguistically relevant properties of a stimulus (such as whether it forms part of one's language or not). Tonal languages provide a particularly valuable window to understand the interplay between these processes. The key to reconciling these phenomena probably lies in understanding the interactions between afferent pathways that carry stimulus information, with top-down processing mechanisms that modulate these processes. Although we are still far from the point of having a complete picture, we argue that moving forward will require us to abandon the dichotomy argument in favour of a more integrated approach.     

Neural representation of spectral and temporal information in speech

Speech is the most interesting and one of the most complex sounds dealt with by the auditory system. The neural representation of speech needs to capture those features of the signal on which the brain depends in language communication. Here we describe the representation of speech in the auditory nerve and in a few sites in the central nervous system from the perspective of the neural coding of important aspects of the signal. The representation is tonotopic, meaning that the speech signal is decomposed by frequency and different frequency components are represented in different populations of neurons. Essential to the representation are the properties of frequency tuning and nonlinear suppression. Tuning creates the decomposition of the signal by frequency, and nonlinear suppression is essential for maintaining the representation across sound levels. The representation changes in central auditory neurons by becoming more robust against changes in stimulus intensity and more transient. However, it is probable that the form of the representation at the auditory cortex is fundamentally different from that at lower levels, in that stimulus features other than the distribution of energy across frequency are analysed.     

Reconstructing speech from human auditory cortex

How the human auditory system extracts perceptually relevant acoustic features of speech is unknown. To address this question, we used intracranial recordings from nonprimary auditory cortex in the human superior temporal gyrus to determine what acoustic information in speech sounds can be reconstructed from population neural activity. We found that slow and intermediate temporal fluctuations, such as those corresponding to syllable rate, were accurately reconstructed using a linear model based on the auditory spectrogram. However, reconstruction of fast temporal fluctuations, such as syllable onsets and offsets, required a nonlinear sound representation based on temporal modulation energy. Reconstruction accuracy was highest within the range of spectro-temporal fluctuations that have been found to be critical for speech intelligibility. The decoded speech representations allowed readout and identification of individual words directly from brain activity during single trial sound presentations. These findings reveal neural encoding mechanisms of speech acoustic parameters in higher order human auditory cortex.     

Hearing feelings: affective categorization of music and speech in alexithymia, an ERP study

Background

Alexithymia, a condition characterized by deficits in interpreting and regulating feelings, is a risk factor for a variety of psychiatric conditions. Little is known about how alexithymia influences the processing of emotions in music and speech. Appreciation of such emotional qualities in auditory material is fundamental to human experience and has profound consequences for functioning in daily life. We investigated the neural signature of such emotional processing in alexithymia by means of event-related potentials.

Methodology

Affective music and speech prosody were presented as targets following affectively congruent or incongruent visual word primes in two conditions. In two further conditions, affective music and speech prosody served as primes and visually presented words with affective connotations were presented as targets. Thirty-two participants (16 male) judged the affective valence of the targets. We tested the influence of alexithymia on cross-modal affective priming and on N400 amplitudes, indicative of individual sensitivity to an affective mismatch between words, prosody, and music. Our results indicate that the affective priming effect for prosody targets tended to be reduced with increasing scores on alexithymia, while no behavioral differences were observed for music and word targets. At the electrophysiological level, alexithymia was associated with significantly smaller N400 amplitudes in response to affectively incongruent music and speech targets, but not to incongruent word targets.

Conclusions

Our results suggest a reduced sensitivity for the emotional qualities of speech and music in alexithymia during affective categorization. This deficit becomes evident primarily in situations in which a verbalization of emotional information is required.     

Recognizing speech in a novel accent: the motor theory of speech perception reframed

The motor theory of speech perception holds that we perceive the speech of another in terms of a motor representation of that speech. However, when we have learned to recognize a foreign accent, it seems plausible that recognition of a word rarely involves reconstruction of the speech gestures of the speaker rather than the listener. To better assess the motor theory and this observation, we proceed in three stages. Part 1 places the motor theory of speech perception in a larger framework based on our earlier models of the adaptive formation of mirror neurons for grasping, and for viewing extensions of that mirror system as part of a larger system for neuro-linguistic processing, augmented by the present consideration of recognizing speech in a novel accent. Part 2 then offers a novel computational model of how a listener comes to understand the speech of someone speaking the listener’s native language with a foreign accent. The core tenet of the model is that the listener uses hypotheses about the word the speaker is currently uttering to update probabilities linking the sound produced by the speaker to phonemes in the native language repertoire of the listener. This, on average, improves the recognition of later words. This model is neutral regarding the nature of the representations it uses (motor vs. auditory). It serve as a reference point for the discussion in Part 3, which proposes a dual-stream neuro-linguistic architecture to revisits claims for and against the motor theory of speech perception and the relevance of mirror neurons, and extracts some implications for the reframing of the motor theory.     

Patterns of covert speech behavior and phonetic coding

In previous research a discriminative relationship has been established between patterns of covert speech behavior and the phonemic system when processing continuous linguistic material. The goal of the present research was to be more analytic and pinpoint covert neuromuscular speech patterns when one processes specific instances of phonemes. Electromyographic (EMG) recording indicated that the lips are significantly active when visually processing the letter "P"(an instance of bilabial material), but not when processing the letter "T" or a nonlinguistic control (C) stimulus. Similarly, the tongue is significantly active when processing the letter "T" (an instance of lingual-alveolar material), but not when processing the letters "P" or "C". It is concluded that the speech musculature covertly responds systematically as a function of class of phoneme being processed. These results accord with our model that semantic processing ("understanding") occurs when the speech (and other) musculature interacts with linguistic regions of the brain. In the interactions phonetic coding is generated and transmitted through neuromuscular circuits that have cybernetic characteristics.     

Acoustic versus phonetic representation of speech as reflected by the mismatch negativity event-related potential

The concept of categorical perception of speech and speech-like sounds has been central to models of speech perception for decades. Event-related potentials (ERPs) provide a neurophysiologic perspective of this important phenomenon. In the present experiment the mismatch negativity (MMN) event-related potential, which is sensitive to fine acoustic differences, was recorded in adults. Of interest was whether the MMN reflects the acoustic or categorical perception of speech.The MMN was elicited by stimulus pairs (along a continuum varying in place of articulation from /da/ to /ga/) which had been identified as the same phoneme /da/ (within category condition) and as different phonemes /da/ and /ga/ (across categories condition). The acoustic differences between these two pairs of stimuli were equivalent.The MMN was observed in all subjects both in the within and across category conditions. Furthermore, the MMN did not differ in latency, amplitude or area within and across categories. That is, the MMN indicated equal discrimination both across and within categories. These results suggest that the MMN appears to reflect the processing of acoustic aspects of the speech stimulus, but not phonetic processing into categories. The MMN appears to be an extremely sensitive electrophysiologic index of minimal acoustic differences in speech stimuli.     

A neural network model with feature selection for Korean speech act classification

A speech act is a linguistic action intended by a speaker. Speech act classification is an essential part of a dialogue understanding system because the speech act of an utterance is closely tied with the user's intention in the utterance. We propose a neural network model for Korean speech act classification. In addition, we propose a method that extracts morphological features from surface utterances and selects effective ones among the morphological features. Using the feature selection method, the proposed neural network can partially increase precision and decrease training time. In the experiment, the proposed neural network showed better results than other models using comparatively high-level linguistic features. Based on the experimental result, we believe that the proposed neural network model is suitable for real field applications because it is easy to expand the neural network model into other domains. Moreover, we found that neural networks can be useful in speech act classification if we can convert surface sentences into vectors with fixed dimensions by using an effective feature selection method.     

Behavior and Approximate Entropy of Right-eye Lateralization During Predation in the Music Frog

Brain asymmetry for processing visual information is widespread in animals. However, it is still unknown how the complexity of the underlying neural network activities represents this asymmetrical pattern in the brain. In the present study, we investigated this complexity using the approximate entropy(ApEn)protocol for electroencephalogram(EEG) recordings from the forebrain and midbrain while the music frogs(Nidirana daunchina) attacked prey stimulus. The results showed that(1) more significant prey responses were evoked by the prey stimulus presented in the right visual field than that in the left visual field,consistent with the idea that right-eye preferences for predatory behaviors exist in animals including anurans;(2) in general, the ApEn value of the left hemisphere(especially the left mesencephalon) was greatest under various stimulus conditions, suggesting that visual lateralization could be reflected by the dynamics of underlying neural network activities and that the stable left-hemisphere dominance of EEG ApEn may play an important role in maintaining this brain asymmetry.     

The mismatch negativity as an index of the perception of speech sounds by the human brain

The present article outlines the contribution of the mismatch negativity (MMN), and its magnetic equivalent MMNm, to our understanding of the perception of speech sounds in the human brain. MMN data indicate that each sound, both speech and non-speech, develops its neural representation corresponding to the percept of this sound in the neurophysiological substrate of auditory sensory memory. The accuracy of this representation, determining the accuracy of the discrimination between different sounds, can be probed with MMN separately for any auditory feature or stimulus type such as phonemes. Furthermore, MMN data show that the perception of phonemes, and probably also of larger linguistic units (syllables and words), is based on language-specific phonetic traces developed in the posterior part of the left-hemisphere auditory cortex. These traces serve as recognition models for the corresponding speech sounds in listening to speech.     

Anatomical correlates of learning novel speech sounds

We examined the relationship between brain anatomy and the ability to learn nonnative speech sounds, as well as rapidly changing and steady-state nonlinguistic sounds, using voxel-based morphometry in 59 healthy adults. Faster phonetic learners appeared to have more white matter in parietal regions, especially in the left hemisphere. The pattern of results was similar for the rapidly changing but not for the steady-state nonlinguistic stimuli, suggesting that morphological correlates of phonetic learning are related to the ability to process rapid temporal variation. Greater asymmetry in the amount of white matter in faster learners may be related to greater myelination allowing more efficient neural processing, which is critical for the ability to process certain speech sounds.     

The Natural Statistics of Audiovisual Speech

Humans, like other animals, are exposed to a continuous stream of signals, which are dynamic, multimodal, extended, and time varying in nature. This complex input space must be transduced and sampled by our sensory systems and transmitted to the brain where it can guide the selection of appropriate actions. To simplify this process, it''s been suggested that the brain exploits statistical regularities in the stimulus space. Tests of this idea have largely been confined to unimodal signals and natural scenes. One important class of multisensory signals for which a quantitative input space characterization is unavailable is human speech. We do not understand what signals our brain has to actively piece together from an audiovisual speech stream to arrive at a percept versus what is already embedded in the signal structure of the stream itself. In essence, we do not have a clear understanding of the natural statistics of audiovisual speech. In the present study, we identified the following major statistical features of audiovisual speech. First, we observed robust correlations and close temporal correspondence between the area of the mouth opening and the acoustic envelope. Second, we found the strongest correlation between the area of the mouth opening and vocal tract resonances. Third, we observed that both area of the mouth opening and the voice envelope are temporally modulated in the 2–7 Hz frequency range. Finally, we show that the timing of mouth movements relative to the onset of the voice is consistently between 100 and 300 ms. We interpret these data in the context of recent neural theories of speech which suggest that speech communication is a reciprocally coupled, multisensory event, whereby the outputs of the signaler are matched to the neural processes of the receiver.     

A sparse neural code for some speech sounds but not for others

The precise neural mechanisms underlying speech sound representations are still a matter of debate. Proponents of 'sparse representations' assume that on the level of speech sounds, only contrastive or otherwise not predictable information is stored in long-term memory. Here, in a passive oddball paradigm, we challenge the neural foundations of such a 'sparse' representation; we use words that differ only in their penultimate consonant ("coronal" [t] vs. "dorsal" [k] place of articulation) and for example distinguish between the German nouns Latz ([lats]; bib) and Lachs ([laks]; salmon). Changes from standard [t] to deviant [k] and vice versa elicited a discernible Mismatch Negativity (MMN) response. Crucially, however, the MMN for the deviant [lats] was stronger than the MMN for the deviant [laks]. Source localization showed this difference to be due to enhanced brain activity in right superior temporal cortex. These findings reflect a difference in phonological 'sparsity': Coronal [t] segments, but not dorsal [k] segments, are based on more sparse representations and elicit less specific neural predictions; sensory deviations from this prediction are more readily 'tolerated' and accordingly trigger weaker MMNs. The results foster the neurocomputational reality of 'representationally sparse' models of speech perception that are compatible with more general predictive mechanisms in auditory perception.     

New strategies for improving speech enhancement

We describe two design strategies that could substantially improve the performance of speech enhancement systems. Results from a preliminary study of pulse recovery are presented to illustrate the potential benefits of such strategies. The first strategy is a direct application of a non-linear, adaptive signal processing approach for recovery of speech in noise. The second strategy optimizes performance by maximizing the enhancement system's ability to evoke target speech percepts. This approach may lead to better performance because the design is optimized on a measure directly related to the ultimate goal of speech enhancement: accurate communication of the speech percept. In both systems, recently developed ‘neural network’ learning algorithms can be used to determine appropriate parameters for enhancement processing.