Longitudinal developmental changes in spectral peaks of vowels produced by Japanese infants

Ishizuka, Kentaro; Mugitani, Ryoko; Kato, Hiroko; Amano, Sadao

doi:10.1121/1.2535806

Cited by 17 publications

(19 citation statements)

References 34 publications

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“…The salience and stability of referent vowels make them easy to encode and hold in memory; this allows them to serve as targets for contrasting with less-focal vowels. These properties also allow Natural Referent vowels to function as production targets that infants can aim for to expand and organize their emerging vowel production skills (Buhr 1980;Lieberman 1980;Kent & Murray 1982;Ishizuka, Mugitani, Kato & Amano 2007) The precise role of Natural Referent Vowels has not been established empirically. We speculate that NRVs may facilitate the recognition of equivalence across vowels produced by different talkers.…”

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confidence: 99%

“…Infants favor front, central vowels in the initial stages of vowel production. However, within a short period their vowel productions expand to encompass a range of vowel qualities that span both dimensions of the F1/F2 space (Buhr 1980;Lieberman 1980;Kent & Murray 1982, Ishizuka et al 2007). The default perceptual frame provided by the NR vowels may provide stable targets to guide the infant's acquisition of a productive vowel space and may be essential for establishing an initial perceptual/motor coupling that is critical for further development.…”

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confidence: 99%

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Natural Referent Vowel (NRV) framework: An emerging view of early phonetic development

Polka

Bohn

2011

Journal of Phonetics

126

207

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The goal of this paper is to provide an overview of an emerging new framework for understanding early phonetic development -The Natural Referent Vowel (NRV) framework. The initial support for this framework was the finding that directional asymmetries occur often in infant vowel discrimination. The asymmetries point to an underlying perceptual bias favoring vowels that fall closer to the periphery of the F1/F2 vowel space. In Polka & Bohn (2003) we reviewed the data on asymmetries in infant vowel perception and proposed that certain vowels act as natural referent vowels and play an important role in shaping vowel perception. In this paper we review findings from studies of infant and adult vowel perception that emerged since Polka & Bohn 2003, from other labs and from our own work, and we formally introduce the NRV framework. We outline how this framework connects with linguistic typology and other models of speech perception and discuss the challenges and promise of NRV as a conceptual tool for advancing our understanding of phonetic development.

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confidence: 99%

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confidence: 99%

Natural Referent Vowel (NRV) framework: An emerging view of early phonetic development

Polka

Bohn

2011

Journal of Phonetics

126

207

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“…Actually, there are large overlaps between vowel /o/ and the others distribution in F1-F2 space for two japanese infants [18]. This suggests that our model reflects the process of vowel acquisition.…”

Section: B Vowel Imitationmentioning

confidence: 76%

Continuous vocal imitation with self-organized vowel spaces in Recurrent Neural Network

Kanda

Ogata

Takahashi

et al. 2009

2009 IEEE International Conference on Robotics and Automation

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Abstract-A continuous vocal imitation system was developed using a computational model that explains the process of phoneme acquisition by infants. Human infants perceive speech sounds not as discrete phoneme sequences but as continuous acoustic signals. One of critical problems in phoneme acquisition is the design for segmenting these continuous speech sounds. The key idea to solve this problem is that articulatory mechanisms such as the vocal tract help human beings to perceive speech sound units corresponding to phonemes. To segment acoustic signal with articulatory movement, we apply the segmenting method to our system by Recurrent Neural Network with Parametric Bias (RNNPB). This method determines the multiple segmentation boundaries in a temporal sequence using the prediction error of the RNNPB model, and the PB values obtained by the method can be encoded as kind of phonemes. Our system was implemented by using a physical vocal tract model, called the Maeda model. Experimental results demonstrated that our system can self-organize the same phonemes in different continuous sounds, and can imitate vocal sound involving arbitrary numbers of vowels using the vowel space in the RNNPB. This suggests that our model reflects the process of phoneme acquisition.

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“…The reason of failure to imitate vowel /o/ was presumably because human infants have difficulties producing this vowel. Actually, there were large overlaps between the distribution of vowel /o/ and the others in F1-F2 space for two Japanese infants [19]. This suggests that our model reflects the process of vowel acquisition.…”

Section: B Vowel Acquisitionmentioning

confidence: 80%

Phoneme acquisition model based on vowel imitation using Recurrent Neural Network

Kanda

Ogata

Takahashi

et al. 2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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-A phoneme-acquisition system was developed using a computational model that explains the developmental process of human infants in the early period of acquiring language. There are two important findings in constructing an infant's acquisition of phonemes: (1) an infant's vowel like cooing tends to invoke utterances that are imitated by its caregiver, and (2) maternal imitation effectively reinforces infant vocalization. Therefore, we hypothesized that infants can acquire phonemes to imitate their caregivers' voices by trial and error, i. e., infants use self-vocalization experience to search for imitable and unimitable elements in their caregivers' voices. On the basis of this hypothesis, we constructed a phoneme acquisition process using interaction involving vowel imitation between a human and an infant model. Our infant model had a vocal tract system, called the Maeda model, and an auditory system implemented by using Mel-Frequency Cepstral Coefficients (MFCCs) through STRAIGHT analysis. We applied Recurrent Neural Network with Parametric Bias (RNNPB) to learn the experience of self-vocalization, to recognize the human voice, and to produce the sound imitated by the infant model. To evaluate imitable and unimitable sounds, we used the prediction error of the RNNPB model. The experimental results revealed that as imitation interactions were repeated, the formants of sounds imitated by our system moved closer to those of human voices, and our system could self-organize the same vowels in different continuous sounds. This suggests that our system can reflect the process of phoneme acquisition. I. INTRODUCTIONOur goal was to clarify how to acquire the ability to distinguish phonemes in the early development of human infants. Infants can acquire spoken language through imitating the vocal output of their parents. This ability is closely related to the cognitive development of language.Developmental psychologists have demonstrated that an infant's vowel-like cooing tends to invoke utterances that are imitated by its caregiver's [1] and that maternal imitation effectively reinforces infant vocalization [2]. Infants have no innate knowledge of phonemes and regard a sound of phoneme sequences as continuous acoustic signals. As they grow, infants acquire the ability to discover phoneme units in continuous speech sounds by prosody, rhythm, stress, and whether they can imitate the sound or not.We hypothesized that infants can acquire phonemes to imitate their caregiver's voices repeatedly by trial and error, i.e., infants use self-vocalization experience to search for H. Kanda, T. Ogata, T. Takahashi, K. Komatani, and H. G. Okuno is with the Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan {hkanda, ogata, tall, komatani, okuno}@kuis.kyoto-u.ac.jp imitable and unimitable elements in their caregiver's voices. We define phoneme acquisition in this paper as follows: Infants can produce sounds close to caregivers' voices.The human-development studies ...

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Longitudinal developmental changes in spectral peaks of vowels produced by Japanese infants

Cited by 17 publications

References 34 publications

Natural Referent Vowel (NRV) framework: An emerging view of early phonetic development

Natural Referent Vowel (NRV) framework: An emerging view of early phonetic development

Continuous vocal imitation with self-organized vowel spaces in Recurrent Neural Network

Phoneme acquisition model based on vowel imitation using Recurrent Neural Network

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