Max msp pitch shift
These observations have been interpreted to mean that at vocal onset, one of the first steps in neural processing of voice auditory feedback is identification of self or non-self vocalization (see Fig. However, if the vocalization is altered in pitch or other characteristics, the ERP it generates is not suppressed relative to the playback and passive listening to the signal. That is to say, vocal onset triggers an ERP that is smaller in magnitude than the ERP triggered by passive listening to the same self-vocalization. When the perturbations are presented at voice onset, there is suppression of the N1 or M1 (MEG) component ( Houde et al., 2002, Heinks-Maldonado et al., 2005) relative to the potential elicited by passive listening to the same pitch-shifted vocalization. The N1 (negative component with peak latency around 100ms post stimulus onset) and P2 (positive component with a peak latency within 200–300ms post stimulus onset) components both show prominent amplitudes to the pitch shift stimulus when the perturbations occur after vocalization onset ( Behroozmand et al., 2009).
Several ERP components are elicited by unexpected shifts in the pitch of self-voice feedback during vocalization. Several studies have examined event-related potentials (ERP’s) in response to varying magnitude of pitch-shifted stimuli during vocalization ( Behroozmand and Larson, 2011 Liu et al., 2011 Korzyukov et al. When presented with a brief shift in vocal pitch of auditory feedback during vocalization, subjects will briefly respond to the perturbation by changing the F0 of their own voice in the opposite direction to the shift. The current study used a pitch-shifted auditory feedback paradigm ( Larson, 1998) where the pitch of self-voice feedback is changed in real time during vocal production. Larson et al., 2000 Jones and Munhall, 2002 Houde and Jordan 2002). Several studies have shown that perturbations in voice auditory feedback are important for both feedforward and feedback control of vocalization. There are clear differences in the neural processing of speaking and listening, and it is well described that attenuation of the auditory cortex is modulated by how closely the auditory feedback from self-vocalization matches expected output ( Houde, et al., 2002 Heinks-Maldonado et al., 2006 Behrozmand and Larson, 2011). One needed critical issue is the understanding of how auditory feedback (the primary sensory system involved in vocalization) is processed and used to control vocal pitch or fundamental frequency (F0) and amplitude. Such information will allow for greater insight into the diagnosis and treatment of voice disorders. While much is known about the human voice, an understanding of the neural basis of voice production is still needed. Moreover, people with voice disorders suffer significant difficulties with communication and interaction with others.
For instance, personality and emotional state are reflected in prosodic features of the voice ( Williams & Stevens, 1972 Tompkins & Mateer, 1985 Banziger & Scherer, 2005). In humans the importance of vocalization cannot be overstated. Vocalization is used to communicate in most air-breathing vertebrate animals and humans. These results also highlight the potential of DCM modeling of ERP responses to characterize specific network properties of forward models of voice control. We identified differences in left to right STG connections between 100 cent and 400 cent shift conditions suggesting that self and non-self voice error are processed differently in the left and right hemisphere. Results suggest that both intrinsic STG and left to right STG connections are important in the identification of self-voice error and sensory motor integration. A Bayesian model selection procedure was used to make inference about model families. We compared three main factors the effect of intrinsic STG connectivity, STG modulation across hemispheres and the specific effect of hemisphere. The ERP data were modeled with Dynamic Causal Modeling (DCM) techniques where the effective connectivity between the superior temporal gyrus (STG), inferior frontal gyrus and premotor areas were tested. Shifts were delivered at +100 and +400 cents (200 ms duration). We presented varying magnitudes of pitch shifted auditory feedback to subjects during vocalization and passive listening and measured event related potentials (ERP’s) to the feedback shifts. We used a pitch-shift paradigm where subjects respond to an alteration, or shift, of voice pitch auditory feedback with a reflexive change in F0. The integration of auditory feedback with vocal motor output is important for the control of voice fundamental frequency (F0).