- OLYMPUS SONORITY TRANSCRIBE BAR NOT SHOWING UP FULL
- OLYMPUS SONORITY TRANSCRIBE BAR NOT SHOWING UP SOFTWARE
In order to address these shortcomings of LENA, an open-source alternative for automatic word count estimation was proposed by Räsänen et al. In addition, LENA is optimized for American English, which means that its absolute word count estimates tend to be more accurate for English than for other languages (though relative counts in within-corpus comparisons are still usually reliable see Räsänen et al., 2019, for a summary). However, LENA is proprietary and expensive, and its core technology is aging with respect to the cutting edge in automated speech processing.
OLYMPUS SONORITY TRANSCRIBE BAR NOT SHOWING UP SOFTWARE
It consists of a physical recording device and associated software for automatically analyzing a number of variables from the data, including estimation of the number of words spoken by adults in the vicinity of the child, in addition to detecting child vocalizations and conversational turns. The existing standard solution to collecting and analyzing child-centered recordings is the widely adopted LENA system (Xu et al., 2008 Gilkerson & Richards, 2009).
OLYMPUS SONORITY TRANSCRIBE BAR NOT SHOWING UP FULL
One key measure of interest is the amount of linguistic input that a child hears within a given time period (e.g., specific time of the day, within the full day, or in a specific environment such as daycare or at home). This means that automatic or semiautomatic tools are essential for processing and analyzing such recordings (see Casillas & Cristia, 2019, for a review). However, the amount of audio data collected with wearable recorders from a population of learners easily surpasses the capacity of any single research lab to comprehensively manually annotate the data for all variables of interest. By using a wearable recorder to capture what children hear in their daily lives, researchers can characterize the quality and quantity of language input and infant–caregiver interaction that children experience, and analyze how such factors may relate to later developmental outcomes (e.g., Gilkerson et al., 2018 Romeo et al., 2018 Suskind et al., 2016 Caskey et al., 2014 Ramírez-Esparza, García-Sierra, & Kuhl, 2014 Weisleder & Fernald, 2013). The use of (day)long child-centered audio recordings from children’s natural environments is becoming one of the standard methods for studying child language acquisition of spoken languages. We share an open-source implementation of ALICE for use by the language research community, enabling automatic phoneme, syllable, and word count estimation from child-centered audio recordings. We show that language-independent measurement of phoneme counts is somewhat more accurate than syllables or words, but all three are highly correlated with human annotations on the same data. We also investigate the practical applicability of measuring such units using a novel system called Automatic LInguistic unit Count Estimator (ALICE) together with audio from seven child-centered daylong audio corpora from diverse cultural and linguistic environments.
We discuss the advantages and disadvantages of measuring different units from theoretical and technical points of view. In this paper, we ask whether some alternative linguistic units, namely phone(me)s or syllables, could be measured instead of, or in parallel with, words in order to achieve improved cross-linguistic applicability and comparability of an automated system for measuring child language input. However, word count estimation is challenging to do in a language- independent manner the relationship between observable acoustic patterns and language-specific lexical entities is far from uniform across human languages. 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. A key measure to quantify from such data is the amount of speech present in children’s home environments. Recordings captured by wearable microphones are a standard method for investigating young children’s language environments.