The Birds and the Babies: Humans and Zebra Finches Have a Similar Technique for Learning to Speak #ASA190

Humans and zebra finches both rely on caregiver reinforcement to learn complex vocalizations.

PHILADELPHIA, May 11, 2026 — We are all born completely helpless, with little of the knowledge and skills we will need to survive as adults. Even our ability to communicate is almost entirely learned from our parents or caregivers.

Some animals are the same. Zebra finches, in particular, are so similar to humans in how they learn to communicate that researchers often study them to better understand how we develop language.

To help answer these questions, Steven Elmlinger, from Princeton University, will present his research on early vocalizations in both human infants and zebra finches Monday, May 11, at 1 p.m. ET as part of the 190th Meeting of the Acoustical Society of America, running May 11-15.

A small finch with gray and orange feathers perches on a wooden bar, looking at an image of a small finch in a cage.

Since zebra finches are similar to humans in how they learn to communicate, researchers can use them as a model for infant language development. Credit: Michael H. Goldstein

Elmlinger studies vocal learning, piecing together how individuals develop and change their vocalizations to interact with others. He conducted three studies, two with human infants and one with finches, to understand how immature babbling transitions into adultlike speech.

In the first experiment, he observed infants interacting with their caregivers, noting that adults respond more to sequential vocalizations comprising multiple syllables than single syllable vocalizations. In the second study, he observed 30 infants across a period of several months to determine the role that these caregivers play in helping infants learn sequential vocalizations.

“We might assume that early learning of vocal sequences is primarily driven by motor practice,” said Elmlinger. “Prior research suggests social feedback guides infants’ vocal advances at the level of individual syllables. Here we wanted to know if social feedback also influences infants’ vocal sequence development.”

In this study, he and his colleagues found that caregiver responses to sequential vocalizations significantly increased the rate at which infants learned to produce those sequences. By encouraging complex vocalizations, caregivers could help their children learn faster.

In the third study, Elmlinger repeated this experiment with zebra finches, with the same finding. Like humans, zebra finches use social feedback to teach their young.

“Taken together, our results suggest that not only do both humans and zebra finches use social feedback to guide advances in the acoustics of their vocal repertoire, but their social environment also guides low-level temporal foundations of their vocal communication,” said Elmlinger.

When it comes to language learning, humans are not as unique as we once thought. At least one other species learns to vocalize in the same way we do, and there may be others awaiting discovery.

“I would love to collaborate with new-world monkey, cetacean, and bat scientists,” said Elmlinger. “There are good hints that at least some of these animals should show socially guided vocal learning as well — perhaps it is not rare, just understudied.”

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New scientific tools help national parks learn more about wildlife and natural sounds

Cathleen Balantic – cathleen_balantic@nps.gov

Biologist, National Park Service, Natural Sounds and Night Skies Division
1201 Oakridge Drive Suite 100
Fort Collins, CO, 80524, United States

Popular version of 2aAB5 – From sounds to science on public lands: using emerging tools in terrestrial bioacoustics to understand national park soundscapes
Presented at the 186th ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0026931

–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–

In recent decades, audio recordings have helped scientists learn more about wildlife. Natural sounds help answer questions such as: which animals are present or absent from the environment? When do frogs and birds start calling in the spring? How are wildlife reacting to something humans are doing on a landscape?

As audio recordings have become less expensive and easier to collect, scientists can rapidly amass thousands of hours of data. To absorb this volume of data, instead of listening ourselves, we create automated detectors to find animal sounds in the recordings. However, it is a daunting and time-consuming task to create detectors for a diversity of species, habitats, and types of research.

This is a familiar challenge to researchers in the National Park Service Natural Sounds and Night Skies Division. Our division is a national service office that provides scientific expertise and specialized technical assistance to parks, and we need to be prepared to help any of the 400+ national parks that have questions about bioacoustics. Each park has distinct research questions, varied habitats, and different wildlife (Fig. 1, Sound Clip 1).

national parks

Figure 1. Varied Thrush at Glacier Bay National Park and Preserve in 2015. Image courtesy of the National Park Service.

Several bird species vocalize at an acoustic monitoring station at Glacier Bay National Park and Preserve, including Pacific Wren, American Robin, and Varied Thrush. This example was recorded on June 13, 2017, at 3:22am local time. Audio recording courtesy of the National Park Service.

As more parks collect audio data to answer pressing research and management questions, building a unique automated detector for a single park project is no longer tenable. Instead, we are adopting emerging technology like BirdNET, a machine learning model trained on thousands of species worldwide (not just birds!). BirdNET provides us with more capacity. Instead of painstakingly building one detector for one project, BirdNET enables us to answer questions across multiple national parks.

But emerging technology poses more questions, too. How do we access these tools? What are the best practices for analyzing and interpreting outputs? How do we adapt new methods to answer many diverse park questions? We don’t all have the answers yet, but now we have code and workflows that help us process terabytes of audio, wrangle millions of rows of output, and produce plots to visualize and explore the data.

We are learning even more by collaborating with other scientists and land managers. So far, we’re exploring avian soundscapes at Glacier Bay National Park and Preserve across a decade of monitoring – from when birds are most vocally active during the spring (Fig.2), to when they are most active during the dawn chorus (Fig. 3). We are learning more about wildlife in the Chihuahuan Desert, wood frogs in Alaska, and how birds respond to simulated beaver structures at Rocky Mountain National Park.

The information we provide and interpret from audio data helps parks understand more about wildlife and actions to protect park resources. Translating huge piles of raw audio data into research insights is still a challenging task, but emerging tools are making it easier.

 

Figure 2. Heat map of BirdNET detection volume for selected focal species at Glacier Bay National Park and Preserve. (a) Hermit Thrush, (b) Pacific-slope Flycatcher, (c) Pacific Wren, (d) Ruby-crowned Kinglet, (e) Townsend’s Warbler, and (f) Varied Thrush. Dates ranging in color from purple to yellow indicate increasing numbers of detections. Dates colored gray had zero detections. White areas show dates where no recordings were collected. Image courtesy of the National Park Service.

 

Figure 3. Heat map of Varied Thrush detections across date and time of day at Glacier Bay National Park and Preserve. Timesteps ranging in color from purple to yellow indicate increasing numbers of detections. Timesteps colored gray had zero detections. White areas show times when no recordings were collected. Audio recordings were scheduled based on sunrise times. Image courtesy of the National Park Service.

Behaviors produced by a variety of sounds among eagles: A study with survival implications

JoAnn McGee – mcgeej@umn.edu

University of Minnesota
75 East River Parkway
Minneapolis, MN 55455
United States

Christopher Feist
Christopher Milliren
Lori Arent
Julia B. Ponder
Peggy Nelson
Edward J. Walsh

Popular version of 3aABb4 – Behavioral responses of bald eagles (Haliaeetus leucocephalus) to acoustic stimuli in a laboratory setting
Presented at the 184 ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0018607
Please keep in mind that the research described in this Lay Language Paper may not have yet been peer reviewed.

The ultimate goal of this project is to protect eagles by discouraging these charismatic birds from entering the airspace of wind energy facilities. The specific question under consideration centers on whether or not an acoustic cue, a sound, can be used for that purpose, to steer eagles away from harm’s way. Our specific goal in this particular study was to take the next step along our overall research path and determine if behaviors of bald eagles in particular were affected by different sound stimuli in a controlled laboratory environment.

Perhaps to be expected, behavioral responses varied significantly. Some birds explored their immediate airspace avidly, while others exhibited a more restrained set of behavioral responses to sound stimulation.

To get a feeling for the task, consider the reaction of this eagle to a sound stimulus in a quiet laboratory setting .

To collect these data, a bird was placed in a sound-damped room and the experiment was conducted from a control center just outside the exposure space. Birds were videotaped as sounds were delivered to one of two speakers and a group of unbiased judges was asked to determine (1) whether the bird responded to the sound based on its behavior, (2) to qualitatively assess the strength of the response, and (3) to identify the behaviors associated with the response. Twelve sounds were tested and judges were instructed to observe the eagle during a specified time window without knowing which sound, if any, had been played. Spectrograms of the sounds tested are shown in the figure.


By far the most common response was an attempt to localize the sound source based on head turning toward a speaker, although birds also frequently tilted their heads in response to stimuli. Females were slightly more responsive to sound stimuli than males, and not surprisingly, stimuli that elicited a higher number of responses also elicited stronger or more evident responses. Complex and natural sounds, for example, sounds produced by eagles, eaglets and pesky mobbing crow sounds, elicited more and stronger responses than man-made stimuli. Generally, bald eagles were fairly accurate in locating the direction that the sound originated, and, as before, females performed better than males.

The results from this study provide a critical step in an effort to protect eagles as we move away from the use of fossil fuels and rely more on wind power. We come away from this study with a better understanding of the types of sound signals that elicit more and stronger responses in bald eagles, and with the confidence that we will be able to objectively assess behavioral responses in more natural settings. We now know what these magnificent birds can hear, and we know that certain sound stimuli are more effective than others in evoking behavioral responses, taking us one step closer to our ultimate goal, to save bald eagles from undesirable outcomes and to give wind facility developers the tools needed to manage their facilities in an even more eco-friendly manner.

3aAB7 – Construction Noise Impact on Wild Birds

Pasquale Bottalico, PhD. – pb@msu.edu

Voice Biomechanics and Acoustics Laboratory
Department of Communicative Sciences and Disorders
College of Communication Arts & Sciences
Michigan State University
1026 Red Cedar Road
East Lansing, MI 48824

Popular version of paper 3aAB7, “Construction noise impact on wild birds”
Presented Tuesday morning, May 25, 2016, 10:20, Salon I
171st ASA Meeting, Salt Lake City

Content
Almost all bird species use acoustic signals to communicate or recognize biological signals – to mate, to detect the sounds of predators and/or prey, to perform mate selection, to defend their territory, and to perform social activities. Noise generated from human activities (in particular by infrastructure and construction sites) has a strong impact on the physiology and behaviour of birds. In this work, a quantitative method for evaluating the impact of noise on wild birds is proposed. The method combines the results of previous studies that considered the effect of noise on birds and involved noise mapping evaluations. A forecast noise simulation was used to generate maps of (1) masking-annoyance areas and (2) potential density variation.

An example of application of the masking-annoyance areas method is shown in Figure 1. If a bird is in the Zone 1 (in purple), traffic noise and construction noise can potentially result in hearing loss and threshold shift. A temporary elevation of the bird’s hearing threshold and a masking of important communication signals can occur in the Zone 2 (in red). Zone 3 (in orange), 4 (in yellow) and 5 (in light green) are characterized by a high, medium and low level of signal masking, respectively. Once the level of noise generated by human activities falls below ambient noise levels in the critical frequencies for communication (2–8 kHz), masking of communication signals is no longer an issue. However, low-frequency noise, such as the rumble of a truck, may still potentially cause other behavioural and/or physiological effects (Zone 6, in green). No effects of any kind occur on the birds in Zone 7 (in dark green). The roles for Zone definition are based on the results of Dooling and Popper. [1]

Bottalico- Birds 1

Figure 1 Mapping of the interaction areas of noise effect on birds within the 7 zones for a project without (a) and with mitigations (b).

Waterman et al. [2] and Reijnem et al. [3-4-5] proposed a trend of the potential variation in birds density in relationship with the noise levels present in the area. This trend shows no effect on density when the noise levels are lower than 45 dB(A), while there is a rapid decrease (with a quadratic shape) for higher levels. An example of the potential decrease in bird density for a project with and without mitigations is shown in Figure 2. The blue areas are the areas where the birds’ density is not influenced by the noise, while the red ones are the areas from where the birds are leaving because the noise levels are too high.

This methodology permits a localization of the areas with greater impacts on birds. The mitigation interventions should be focused on these areas in order to balance bird habitat conservation and human use of land.

Bottalico- Birds 2

Figure 2 Potential decrease in bird density for a project without (a) and with mitigations (b).

 

References

  1. R. J. Dooling and A. N. Popper, The effects of highway noise on birds, Report prepared for The California Department of Transportation Division of Environmental Analysis, (2007).
  2. E. Waterman, I. Tulp, R. Reijnen, K. Krijgsveld and C. ter Braak, “Noise disturbance of meadow birds by railway noise”, Inter-Noise2004, (2004).
  3. R. Reijnen and R. Foppen, “The effects of car traffic on breeding bird populations in woodland. IV. Influence of population size on the reduction of density close to the highway”, J. Appl. Ecol. 32(3), 481-491, (1995).
  4. R. Reijnen, R. Foppen, C. ter Braak and J. Thissen, “The effects of car traffic on breeding bird populations in Woodland. III. Reduction of density in relation to the proximity of main roads”, J. Appl. Ecol. 32(1), 187-202, (1995).
  5. R. Reijnen, G. Veenbaas and R. Foppen, Predicting the Effects of Motorway Traffic on Breeding Bird Populations. Ministry of Transport and Public Works, Delft, Netherlands, (1995).

4aAB2 – Seemingly simple songs: Black-capped chickadee song revisited

Allison H. Hahn – ahhahn@ualberta.ca
Christopher B. Sturdy – csturdy@ualberta.ca

University of Alberta
Edmonton, AB, Canada

Popular version of 4aAB2 – Seemingly simple songs: Black-capped chickadee song revisited
Presented Thursday morning, November 5, 8:55 AM, City Terrace Room
170th ASA Meeting, Jacksonville, Fl

Vocal communication is a mode of communication important to many animal species, including humans. Over the past 60 years, songbird vocal communication has been widely-studied, largely because the invention of the sound spectrograph allows researchers to visually represent vocalizations and make precise acoustic measurements. Black-capped chickadees (Poecile atricapillus; Figure 1) are one example of a songbird whose song has been well-studied. Black-capped chickadees produce a short (less than 2 seconds), whistled fee-bee song. Compared to the songs produced by many songbird species, which often contain numerous note types without a fixed order, black-capped chickadee song is relatively simple, containing two notes produced in the same order during each song rendition. Although the songs appear to be acoustically simple, they contain a rich variety of information about the singer including: dominance rank, geographic location, and individual identity [1,2,3].

Interestingly, while songbird song has been widely-examined, most of the focus (at least for North Temperate Zone species) has been on male-produced song, largely because it was thought that only males actually produced song. However, more recently, there has been mounting evidence that in many songbird species, both males and females produce song [4,5]. In the study of black-capped chickadees, the focus has also been on male-produced song. However, recently, we reported that female black-capped chickadees also produce fee-bee song. One possible reason that female song has not been extensively reported is that to human vision, male and female chickadees are visually identical, so females that are singing may be mistakenly identified as male. However, by identifying a bird’s sex (via DNA analysis) and recording both males and females, our work [6] has shown that female black-capped chickadees do produce fee-bee song. Additionally, these songs are overall acoustically similar to male song (songs of both sexes contain two whistled notes; see Figure 2), making vocal discrimination by humans difficult.

Our next objective was to determine if any acoustic features varied between male and female songs. Using bioacoustic techniques, we were able to demonstrate that there are acoustic differences in male and female song, with females producing songs that contain a greater frequency decrease in the first note compared to male songs (Figure 2). These results demonstrate that there are sufficient acoustic differences to allow birds to identify the sex of a signing individual even in the absence of visual cues. Because birds may live in densely wooded environments, in which visual, but not auditory, cues are often obscured, being able to identify the sex of a bird (and whether the singer is a potential mate or territory rival) would be an important ability.

Following our bioacoustic analysis, an important next step was to determine whether birds are able to distinguish between male and female songs. In order to examine this, we used a behavioral paradigm that is common in animal learning studies: operant conditioning. By using this task, we were able to demonstrate that birds can distinguish between male and female songs; however, the particular acoustic features birds use in order to discriminate between the sexes may depend on the sex of the bird that is listening to the song. Specifically, we found evidence that male subjects responded based on information in the song’s first note, while female subjects responded based on information in the song’s second note [7]. One possible reason for this difference in responding is that in the wild, males need to quickly respond to a rival male that is a territory intruder, while females may assess the entire song to gather as much information about the singing individual (for example, information regarding a potential mate’s quality). While the exact function of female song is unknown, our studies clearly indicate that female black-capped chickadees produce songs and the birds themselves can perceive differences between male and female songs.

Black-capped chickadee
Figure 1. An image of a black-capped chickadee.

Sturdy_Figure2
Figure 2. Spectrogram (x-axis: time; y-axis: frequency in kHz) on a male song (top) and female song (bottom).

Sound file 1. An example of a male fee-bee song.

Sound file 2. An example of a female fee-bee song.

References

  1. Hoeschele, M., Moscicki, M.K., Otter, K.A., van Oort, H., Fort, K.T., Farrell, T.M., Lee, H., Robson, S.W.J., & Sturdy, C.B. (2010). Dominance signalled in an acoustic ornament. Animal Behaviour, 79, 657–664.
  2. Hahn, A.H., Guillette, L.M., Hoeschele, M., Mennill, D.J., Otter, K.A., Grava, T., Ratcliffe, L.M., & Sturdy, C.B. (2013). Dominance and geographic information contained within black-capped chickadee (Poecile atricapillus) song. Behaviour, 150, 1601-1622.
  3. Christie, P.J., Mennill, D.J., & Ratcliffe, L.M. (2004). Chickadee song structure is individually distinctive over long broadcast distances. Behaviour 141, 101–124.
  4. Langmore, N.E. (1998). Functions of duet and solo songs of female birds. Trends in Ecology and Evolution, 13, 136–140.
  5. Riebel, K. (2003). The “mute” sex revisited: vocal production and perception learning in female songbirds. Advances in the Study of Behavior, 33, 49–86
  6. Hahn, A.H., Krysler, A., & Sturdy, C.B. (2013). Female song in black-capped chickadees (Poecile atricapillus): Acoustic song features that contain individual identity information and sex differences. Behavioural Processes, 98, 98-105.
  7. Hahn, A.H., Hoang, J., McMillan, N., Campbell, K., Congdon, J., & Sturdy, C.B. (2015). Biological salience influences performance and acoustic mechanisms for the discrimination of male and female songs. Animal Behaviour, 104, 213-228.