Influence of Sound Immersion and Communicative Interaction on the Lombard Effect

Garnier, Maëva; Henrich, Nathalie; Dubois, Danièle

doi:10.1044/1092-4388(2009/08-0138)

Cited by 157 publications

(176 citation statements)

References 52 publications

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“…However, the term reflex should not be interpreted as that the Lombard effect is a fixed response to background noise. In fact, the Lombard effect in humans is affected by both the communication context (30) and content (31) and can be inhibited through training (32). These studies highlight that cognitive processes can modulate the Lombard effect.…”

Section: Discussionmentioning

confidence: 99%

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Sensorimotor integration on a rapid time scale

Luo

Kothari

Moss

2017

Proc. Natl. Acad. Sci. U.S.A.

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Sensing is fundamental to the control of movement: From grasping objects to speech production, sensing guides action. So far, most of our knowledge about sensorimotor integration comes from visually guided reaching and oculomotor integration, in which the time course and trajectories of movements can be measured at a high temporal resolution. By contrast, production of vocalizations by humans and animals involves complex and variable actions, and each syllable often lasts a few hundreds of milliseconds, making it difficult to infer underlying neural processes. Here, we measured and modeled the transfer of sensory information into motor commands for vocal amplitude control in response to background noise, also known as the Lombard effect. We exploited the brief vocalizations of echolocating bats to trace the time course of the Lombard effect on a millisecond time scale. Empirical studies revealed that the Lombard effect features a response latency of a mere 30 ms and provided the foundation for the quantitative audiomotor model of the Lombard effect. We show that the Lombard effect operates by continuously integrating the sound pressure level of background noise through temporal summation to guide the extremely rapid vocal-motor adjustments. These findings can now be extended to models and measures of audiomotor integration in other animals, including humans.S ensing plays a critical role in the control of movement. In humans, natural behaviors, from grasping a coffee mug to producing intelligible speech, all rely on the guidance of sensing. Similarly, sensory signals lie at the core of most animal behaviors. However, the brain mechanisms underlying sensorimotor integration are not well understood. This is particularly true regarding the control of vocalizations, which are used by a wide range of animal species for communication. At present, a dominant model for motor control is derived from the state feedback control (SFC) theory, which successfully accounts for a wide range of motor behaviors, such as visually guided arm movement (1) and speech production (2). SFC models posit that motor control is based on a comparison of sensory prediction generated from an internal forward model with actual sensory feedback, and sensory feedback is used to train and update the internal forward model. According to this SFC model, sensory feedback is not directly used to guide motor commands, due to its noisy and delayed characteristics, and this notion has been supported by a large body of experimental and theoretical work (1-7). These stand in contrast to motor reflexes, which are movements in direct response to sensory signals. Note, motor reflexes can be graded in response magnitude and can be modulated by cognitive processes (8, 9). One well-known example is the pupillary light reflex, an adjustment in pupil diameter in response to light intensity (10).The Lombard effect refers to an animal's increase in vocal signal amplitude in response to an increase in background noise (11). Evidence of the Lombard effect comes from s...

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Section: Discussionmentioning

confidence: 99%

“…Optimization for each of the three noise conditions (30,40, and 50 dB SPL) was run repeatedly until the optimization output did not improve anymore. Specifically, we used the mean vocal-level adjustments, as shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Sensorimotor integration on a rapid time scale

Luo

Kothari

Moss

2017

Proc. Natl. Acad. Sci. U.S.A.

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“…Alternatively, participants may not have been motivated to maximize their communicative efforts (despite being told they were being scored for intelligibility) because they were vocalizing on their own in a darkened room. Although Lombard speech occurs in the absence of a conversational partner, it is significantly modulated by communicative intent (Garnier et al, 2010). Since exploring communicative adaptations is of critical interest here, it is important to develop more interactive experimental paradigms-perhaps allowing the participant to directly speak to a partner in the control room via audio or video link-up.…”

Section: Discussionmentioning

confidence: 99%

“…Talkers are better at retiming their voices to accommodate spectral and amplitude dips in a speech masker, as compared to speech modulated noise (which has the same kind of amplitude dips, but no intelligible content). Although speaking in noise reliably causes vocal adaptation, the degree to which talkers change their voice is highly situation-dependent, with the greatest response always evoked by communicative contexts (Cooke and Lu, 2010;Garnier et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Distinct neural systems recruited when speech production is modulated by different masking sounds

Meekings¹,

Evans²,

Lavan³

et al. 2016

The Journal of the Acoustical Society of America

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When talkers speak in masking sounds, their speech undergoes a variety of acoustic and phonetic changes. These changes are known collectively as the Lombard effect. Most behavioural research and neuroimaging research in this area has concentrated on the effect of energetic maskers such as white noise on Lombard speech. Previous fMRI studies have argued that neural responses to speaking in noise are driven by the quality of auditory feedback—that is, the audibility of the speaker's voice over the masker. However, we also frequently produce speech in the presence of informational maskers such as another talker. Here, speakers read sentences over a range of maskers varying in their informational and energetic content: speech, rotated speech, speech modulated noise, and white noise. Subjects also spoke in quiet and listened to the maskers without speaking. When subjects spoke in masking sounds, their vocal intensity increased in line with the energetic content of the masker. However, the opposite pattern was found neurally. In the superior temporal gyrus, activation was most strongly associated with increases in informational, rather than energetic, masking. This suggests that the neural activations associated with speaking in noise are more complex than a simple feedback response.

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“…This finding emphasizes the dynamics of the Lombard effect. In humans, the expression of the Lombard effect is affected by the content (Patel and Schell, 2008) and the context (Garnier et al, 2010) of the speech. Specifically, humans show a greater Lombard magnitude for information-bearing contents than for non-information-bearing contents, and a greater compensation magnitude for communicative tasks than reading tasks.…”

Section: Discussionmentioning

confidence: 99%

The Lombard effect emerges early in young bats: Implications for the development of audio-vocal integration

Luo

Lingner

Firzlaff

et al. 2016

Journal of Experimental Biology

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Auditory feedback plays an important role in vocal learning and, more generally, in fine-tuning the acoustic features of communication signals. So far, only a few studies have assessed the developmental onset of auditory feedback. The Lombard effect, a well-studied audiovocal phenomenon, refers to an increase in vocal loudness of a subject in response to an increase in background noise. Here, we studied the time course of the Lombard effect in developing bats, Phyllostomus discolor. We show that infant bats produced louder vocalizations in noise than in silence at an age of only 2 weeks. In contrast, the infant bats' morphology and vocalizations changed gradually until 2 months of age. Furthermore, we found that the Lombard magnitude, i.e. how much the bats increased their vocal loudness in noise relative to silence, correlated positively with the age of the infant bats. We conclude that the Lombard effect features an early developmental origin, indicating a fast maturation of the underlying neural circuits for audio-vocal feedback.

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Influence of Sound Immersion and Communicative Interaction on the Lombard Effect

Cited by 157 publications

References 52 publications

Sensorimotor integration on a rapid time scale

Sensorimotor integration on a rapid time scale

Distinct neural systems recruited when speech production is modulated by different masking sounds

The Lombard effect emerges early in young bats: Implications for the development of audio-vocal integration

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