Phonology Analysis From Childrens' Speech
Date
2022
Authors
Bhaskar, Ramteke Pravin
Journal Title
Journal ISSN
Volume Title
Publisher
National Institute of Technology Karnataka, Surathkal
Abstract
Human vocal tract can produce various sounds. The speech sounds are relatively a very
small set of such sounds that appears uniquely quali ed to be used in the production
of speech. It includes positions of the parts of the body necessary for producing spoken
words and the e ect of air rushing from lungs as it passes through the larynx, pharynx,
vocal cords, nasal passages and mouth. Phonetic sounds (phones) are the actual speech
sounds classi ed by the manner and place of articulation (i.e. the way in which air is forced
through the mouth and shaped by the tongue, teeth, palate, lips and in some languages by
the uvula). Children begin language acquisition with their rst meaningful word. Further,
they acquire language by mimicking the adult pronunciation. This development mainly
depends on the development of vocal tract, neuro-motor control and in uence from the
language of people surrounding them. Signi cant di erence can be observed in the vocal
tract of the child and adult where the vocal tract in children is underdeveloped and short in
comparison with the adult vocal tract. Along with these, other oral cavity parameters such
as tongue, larynx, epiglottis, vocal cords are also underdeveloped. Due to this, children
face di culty in producing speech sounds, where the pronunciations are simpli ed by
substituting the di cult speech sounds with other simple one. This results in signi cant
deviations and replacements in the pronunciation of phonemes in children leading to
mispronunciation or pronunciation errors. These processes are referred to as phonological
processes. The phonological processes appear in the children represents the agewise speech
learning ability. The analysis helps the Speech Language Pathologists (SLPs) in studying
language learning ability of the children. The manual process of phonology analysis
involves lot of human e ort and time. Literature reports that the phonological processes
are properly studied in the children speaking English as native language. Indian languages
are syllabic in nature and di er from English which is phonemic in nature. Hence, the
observations made in the case of English children may not be directly applicable to the
study of phonological developments observed in the case of Indian children. In general,
the appearance of phonological processes in the case of Indian children is not well studied
i
and documented. The appearance of these processes beyond certain age may indicate
the presence of the phonological disorder. It helps the SLPs to automatically identify
the processes and analyse the language learning pattern along with disorders present if
processes are observed beyond certain age.
In this work, we aim to develop the systems for automatic identi cation of phonological
processes in Kannada language. Applications of this research work include evaluation
of language learning ability, identi cation of speech and motor disorder, gender based
analysis of phonological processes, etc. Some of the important issues in this research area
are, large number of non-standardized phonological processes; lack of detailed studies in
Indian languages; availability of children's speech databases in the required age range from
31
2 to 61
2 years; di culties in adapting existing systems of mispronunciation identi cation
due to huge di erence in the speech production parameters of the adults and children
for the proposed age range; need of identifying features characterizing each phonological
process in comparison based algorithms. We recorded Kannada language speech dataset
from children between age 3 1
2 to 61
2 years and named it as NITK Kids' Speech Corpus.
It is collected in three age groups with an interval of one year in each age group. For
each age range, the data is recorded from 40 children (20 male and 20 female). This work
provides, the detailed analysis of the phonological processes that appear in children from
age 3 1
2 years to 6 1
2 years speaking Kannada as native language. Based on the pattern
of disappearance of the phonological process, the age-wise analysis of the acquisition of
phonemes is provided. A detailed comparison of language learning ability of the children
speaking English language and Kannada language is also performed.
Based on the e ectiveness of the comparison based algorithms in identi cation of
phonological processes in smaller age range, it is considered for the analysis. Commonly
observed phonological processes that are considered for our study are: aspiration, nasal-
ization & nasal assimilation, palatal fricative fronting, nal consonant deletion, voicing
assimilation and vowel deviations. Spectral, prosodic and excitation source features ef-
cient in discriminating the correct pronunciation of a phoneme and its mispronounced
counterpart are identi ed and exploited for the identi cation of phonological processes.
Two case studies are considered for the evaluation. Based on the availability of the
dataset for phonological disorder, 'rhotacism' is considered for the analysis. The spec-
tral and prosodic features e cient in characterization of the phonological disorder are
explored. During the processes of phonological process identi cation, we came across
ii
interesting problem of children gender identi cation. The task of gender identi cation
from children's speech is di cult compared to adult gender identi cation. The gender
identi cation from adult speech is also performed to analyze the di culties in the task
of children gender identi cation in comparison with the adult speech. The role of spec-
tral, prosodic, excitation source features have been proposed gender identi cation in both
implementations using suitable machine learning algorithms. Detailed experimental eval-
uation is carried out to compare the performance of each of the proposed approaches
against baseline and state-of-the-art systems.
Description
Keywords
Aspiration, Excitation source features, Gender identi cation, Machine learning