Marvin Lavechin
Biographic Data
| ID | 6537031 |
|---|---|
| NAME | Marvin Lavechin |
| GIVEN NAMES | Marvin |
| FAMILY NAME | Lavechin |
| SIGNATURE | LAVECHIN M |
| AFFILIATIONS | Centre National de la Recherche Scientifique |
| ORCID | 0000-0002-6005-9368 |
| VERIFIED | Yes |
| TOTAL WORKS | 16 |
| TOTAL CITATIONS | 2 |
| WORKS AS AUTHOR | 16 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Simulating prenatal language exposure in computational models
Researchers have hypothesized that infant language learning starts from the third trimester of pregnancy. This is supported by studies with fetuses and newborns showing discrimination/preference for their native language. Jointly with empirical research, initial computational modeling studies have investigated whether learning language patterns from speech input benefits from auditory prenatal language exposure (PLE), showing some advantages for …
Simulating Early Phonetic and Word Learning Without Linguistic Categories
Before they even talk, infants become sensitive to the speech sounds of their native language and recognize the auditory form of an increasing number of words. Traditionally, these early perceptual changes are attributed to an emerging knowledge of linguistic categories such as phonemes or words. However, there is growing skepticism surrounding this interpretation due to limited evidence of category knowledge in infants. Previous modeling work ha…
Modeling early phonetic acquisition from child-centered audio data
Modeling the initial state of early phonetic learning in infants
What are the necessary conditions to acquire language? Do infants rely on simple statistical mechanisms, or do they come pre-wired with innate capabilities allowing them to learn their native language(s)? Previous modeling studies have shown that unsupervised learning algorithms could reproduce some aspects of infant phonetic learning. Despite these successes, algorithms still fail to reproduce the learning trajectories observed in infants. Here,…
Establishing the reliability of metrics extracted from long-form recordings using Lena and the Aclew pipeline
Long-form audio recordings are increasingly used to study individual variation, group differences, and many other topics in theoretical and applied fields of developmental science, particularly for the description of children's language input (typically speech from adults) and children's language output (ranging from babble to sentences). The proprietary LENA software has been available for over a decade, and with it, users have come to rely on d…
Decode, Move and Speak! Self-supervised Learning of Speech Units, Gestures, and Sound Relationships Using Vocal Imitation
Speech learning encompasses mastering a complex motor system to produce speech sounds from articulatory gestures while simultaneously uncovering discrete units that provide entry to the linguistic system. Remarkably, children acquire these associations between speech sounds, articulatory gestures, and linguistic units in a weakly supervised manner, without the need for explicit labeling of auditory inputs or access to target articulatory gestures…
Modeling early phonetic acquisition from child-centered audio data
Infants learn their native language(s) at an amazing speed. Before they even talk, their perception adapts to the language(s) they hear. However, the mechanisms responsible for this perceptual attunement and the circumstances in which it takes place remain unclear. This paper presents the first attempt to study perceptual attunement using ecological child-centered audio data. We show that a simple prediction algorithm exhibits perceptual attuneme…
Realistic and broad-scope learning simulations
There is a current 'theory crisis' in language acquisition research, resulting from fragmentation both at the level of the approaches and the linguistic level studied. We identify a need for integrative approaches that go beyond these limitations, and propose to analyse the strengths and weaknesses of current theoretical approaches of language acquisition. In particular, we advocate that language learning simulations, if they integrate realistic …
Statistical learning models of early phonetic acquisition struggle with child-centered audio data
Infants learn their native language(s) at an amazing speed. Before they even talk, their perception adapts to the language(s) they hear. However, the mechanisms responsible for this perceptual attunement still remain unclear. A long tradition in linguistics points to the importance of specialized language mechanisms that would allow us to quickly and effortlessly learn from the language(s) we are exposed to. However, the currently dominant explan…
Can statistical learning bootstrap early language acquisition? A modeling investigation
Before they even produce their first word, infants start developing a language-specific perception, recognize the auditory form of frequent words, and develop a rudimentary knowledge of grammatical categories. A major question in language development is: what mechanisms are responsible for the effortless learning infants demonstrate? In-laboratory experiments have shown that young infants are exquisitely sensitive to fine-grained statistical regu…
Reverse engineering language acquisition with child-centered long-form recordings
Language use in everyday life can be studied using lightweight, wearable recorders that collect long-form recordings - that is, audio (including speech) over whole days. The hardware and software underlying this technique is increasingly accessible and inexpensive, and these data are revolutionizing the language acquisition field. We first place this technique into the broader context of the current ways of studying both the input being received …
Reverse Engineering Language Acquisition with Child-Centered Long-Form Recordings
Language use in everyday life can be studied using lightweight, wearable recorders that collect long-form recordings—that is, audio (including speech) over whole days. The hardware and software underlying this technique are increasingly accessible and inexpensive, and these data are revolutionizing the language acquisition field. We first place this technique into the broader context of the current ways of studying both the input being received b…
Alice
Recordings captured by wearable microphones are a standard method for investigating young children’s language environments. A key measure to quantify from such data is the amount of speech present in children’s home environments. To this end, the LENA recorder and software—a popular system for measuring linguistic input—estimates the number of adult words that children may hear over the course of a recording. However, word count estimation is cha…
Alice
Recordings captured by wearable microphones are a standard method for investigating young children's language environments. A key measure to quantify from such data is the amount of speech present in children's home environments. To this end, the LENA recorder and software-a popular system for measuring linguistic input-estimates the number of adult words that children may hear over the course of a recording. However, word count estimation is cha…
A thorough evaluation of the Language Environment Analysis (Lena) system
A thorough evaluation of the Language Environment Analysis (Lenatm) system
In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENA^®^ system, which aims to assess children's language environment. While the system's prevalence in the language acquisition domain is steadily growing, there are only scattered validation efforts, on only some of its key characteristics. H…
Reverse Engineering Language Acquisition with Child-Centered Long-Form Recordings
Language use in everyday life can be studied using lightweight, wearable recorders that collect long-form recordings—that is, audio (including speech) over whole days. The hardware and software underlying this technique are increasingly accessible and inexpensive, and these data are revolutionizing the language acquisition field. We first place this technique into the broader context of the current ways of studying both the input being received b…
A thorough evaluation of the Language Environment Analysis (Lenatm) system
In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENA^®^ system, which aims to assess children's language environment. While the system's prevalence in the language acquisition domain is steadily growing, there are only scattered validation efforts, on only some of its key characteristics. H…
Alice
Recordings captured by wearable microphones are a standard method for investigating young children’s language environments. A key measure to quantify from such data is the amount of speech present in children’s home environments. To this end, the LENA recorder and software—a popular system for measuring linguistic input—estimates the number of adult words that children may hear over the course of a recording. However, word count estimation is cha…
Alice
Recordings captured by wearable microphones are a standard method for investigating young children's language environments. A key measure to quantify from such data is the amount of speech present in children's home environments. To this end, the LENA recorder and software-a popular system for measuring linguistic input-estimates the number of adult words that children may hear over the course of a recording. However, word count estimation is cha…
A thorough evaluation of the Language Environment Analysis (Lena) system
Reverse engineering language acquisition with child-centered long-form recordings
Language use in everyday life can be studied using lightweight, wearable recorders that collect long-form recordings - that is, audio (including speech) over whole days. The hardware and software underlying this technique is increasingly accessible and inexpensive, and these data are revolutionizing the language acquisition field. We first place this technique into the broader context of the current ways of studying both the input being received …
Reverse Engineering Language Acquisition with Child-Centered Long-Form Recordings
Language use in everyday life can be studied using lightweight, wearable recorders that collect long-form recordings—that is, audio (including speech) over whole days. The hardware and software underlying this technique are increasingly accessible and inexpensive, and these data are revolutionizing the language acquisition field. We first place this technique into the broader context of the current ways of studying both the input being received b…
Statistical learning models of early phonetic acquisition struggle with child-centered audio data
Infants learn their native language(s) at an amazing speed. Before they even talk, their perception adapts to the language(s) they hear. However, the mechanisms responsible for this perceptual attunement still remain unclear. A long tradition in linguistics points to the importance of specialized language mechanisms that would allow us to quickly and effortlessly learn from the language(s) we are exposed to. However, the currently dominant explan…
Can statistical learning bootstrap early language acquisition? A modeling investigation
Before they even produce their first word, infants start developing a language-specific perception, recognize the auditory form of frequent words, and develop a rudimentary knowledge of grammatical categories. A major question in language development is: what mechanisms are responsible for the effortless learning infants demonstrate? In-laboratory experiments have shown that young infants are exquisitely sensitive to fine-grained statistical regu…
Modeling early phonetic acquisition from child-centered audio data
Infants learn their native language(s) at an amazing speed. Before they even talk, their perception adapts to the language(s) they hear. However, the mechanisms responsible for this perceptual attunement and the circumstances in which it takes place remain unclear. This paper presents the first attempt to study perceptual attunement using ecological child-centered audio data. We show that a simple prediction algorithm exhibits perceptual attuneme…
Realistic and broad-scope learning simulations
There is a current 'theory crisis' in language acquisition research, resulting from fragmentation both at the level of the approaches and the linguistic level studied. We identify a need for integrative approaches that go beyond these limitations, and propose to analyse the strengths and weaknesses of current theoretical approaches of language acquisition. In particular, we advocate that language learning simulations, if they integrate realistic …
Modeling early phonetic acquisition from child-centered audio data
Modeling the initial state of early phonetic learning in infants
What are the necessary conditions to acquire language? Do infants rely on simple statistical mechanisms, or do they come pre-wired with innate capabilities allowing them to learn their native language(s)? Previous modeling studies have shown that unsupervised learning algorithms could reproduce some aspects of infant phonetic learning. Despite these successes, algorithms still fail to reproduce the learning trajectories observed in infants. Here,…
Establishing the reliability of metrics extracted from long-form recordings using Lena and the Aclew pipeline
Long-form audio recordings are increasingly used to study individual variation, group differences, and many other topics in theoretical and applied fields of developmental science, particularly for the description of children's language input (typically speech from adults) and children's language output (ranging from babble to sentences). The proprietary LENA software has been available for over a decade, and with it, users have come to rely on d…
Decode, Move and Speak! Self-supervised Learning of Speech Units, Gestures, and Sound Relationships Using Vocal Imitation
Speech learning encompasses mastering a complex motor system to produce speech sounds from articulatory gestures while simultaneously uncovering discrete units that provide entry to the linguistic system. Remarkably, children acquire these associations between speech sounds, articulatory gestures, and linguistic units in a weakly supervised manner, without the need for explicit labeling of auditory inputs or access to target articulatory gestures…
Simulating prenatal language exposure in computational models
Researchers have hypothesized that infant language learning starts from the third trimester of pregnancy. This is supported by studies with fetuses and newborns showing discrimination/preference for their native language. Jointly with empirical research, initial computational modeling studies have investigated whether learning language patterns from speech input benefits from auditory prenatal language exposure (PLE), showing some advantages for …
Simulating Early Phonetic and Word Learning Without Linguistic Categories
Before they even talk, infants become sensitive to the speech sounds of their native language and recognize the auditory form of an increasing number of words. Traditionally, these early perceptual changes are attributed to an emerging knowledge of linguistic categories such as phonemes or words. However, there is growing skepticism surrounding this interpretation due to limited evidence of category knowledge in infants. Previous modeling work ha…
Computer Science (15 works) · Psychology (14 works) · Language Development and Disorders (13 works) · Language acquisition (10 works) · Speech and dialogue systems (9 works) · Artificial Intelligence (8 works) · Natural language processing (7 works) · Mathematics education (6 works) · Speech and Audio Processing (6 works) · Speech recognition (6 works)