scholarly journals Od nagrania do korpusu, czyli o metodzie archiwizowania języka mówionego mieszkańców wsi z wykorzystaniem narzędzi lingwistyki cyfrowej

2021 ◽  
pp. 54-67
Author(s):  
Helena Grochola-Szczepanek
Keyword(s):  
Spoken Language ◽  
Standard Code ◽  
Language Data ◽  
Multi Phase ◽  
Language Corpus ◽  
Phase Process

The article presents the method of archiving of the rural speech during the development of the electronic language corpus. Attention is focused on how to get spoken data and transcription of non-standard dialect code. It also presents the problems and limitations resulting from nonnormative spoken data and the solutions applied. The recording and converting of spoken language data for corpus is a complex and multi-phase process. The data is obtained from recorded interviews with respondents. The developed system of spoken data transcription combines the properties of non-standard code, the capabilities of tools and needs of corpus.

Embracing multidimensionality in phonological analysis

2021 ◽  
Vol 38 (1) ◽  
pp. 101-139
Author(s):  
Abigail C. Cohn ◽  
Margaret E. L. Renwick
Keyword(s):  
Native Speaker ◽  
Experimental Studies ◽  
Spoken Language ◽  
Iterative Approach ◽  
Full Spectrum ◽  
Multiple Factors ◽  
Multiple Data ◽  
Language Data ◽  
Corpus Data ◽  
Insight Into

Abstract We pursue the idea, implicit in much current phonological research, that understanding the multiple factors that shape speech production and perception is within the purview of phonology. In particular, increased access to naturalistic data has highlighted the multidimensional reality of variation in spoken language. At the same time, longstanding methods of doing phonology – including impressionistic analysis, and laboratory and experimental studies – remain crucial to understanding native speaker competence and grammar. We advocate for an expanded methodological toolbox in phonological analysis, using an iterative approach that crucially includes naturalistic corpus data. Integrating across multiple data sources offers fuller insight into the nature of the phonological system and native speaker-hearer ability. Several case studies highlight findings gained through linked, iterative studies, showing the importance of naturalistic data for a richer understanding of phonological phenomena, and leading us to reflect on desiderata for corpora to reveal speaker-specific patterns in fine phonetic detail and variability, which we argue are part of a speaker-hearer’s phonological competence. Phonological analysis that embraces the full spectrum of variation in spoken language data (from categorical to gradient, and systematic to sporadic) contributes to a deeper understanding of phonology in this richer sense.

The Operation of Cool Edit Pro in Corpus-Based Spoken Language

2013 ◽  
Vol 694-697 ◽  
pp. 2383-2387
Author(s):  
Jie Chen ◽  
Jing Chen
Keyword(s):  
Sound Wave ◽  
Native Speaker ◽  
Spoken Language ◽  
Short Wave ◽  
Wave Crest ◽  
Sharp Wave ◽  
Wave Trough ◽  
Long Wave ◽  
Autonomic Learning ◽  
Language Corpus

Corpus refers to the database of language materials. Cool Edit Pro is a media edit software. This paper explores how to construct spoken language corpus, how to use cool edit pro 2 to make sound wave contrast and give the experimenters an intuitive observation from their own speech waveforms. The key is to offer the obvious waveforms contrast among the sampling waveform of the native speaker, the original and unmodified one of the experimenter and the new waveform of the experimenter after modifications and teachers instructions, which makes the oral autonomic learning more possible and scientific. From long wave or short wave, wave trough or wave crest, smooth wave or sharp wave, the experimenters deviations can be easily identified from the standard during the autonomic practices and efficiently make corrections. Additionally, experimenter also can observe the improvements frequently, which means this experiment more instructive.

scholarly journals The Listening and Spoken Language Data Repository: Design and Project Overview

2018 ◽  
Vol 49 (1) ◽  
pp. 108-120 ◽  
Author(s):  
Tamala S. Bradham ◽  
Christopher Fonnesbeck ◽  
Alice Toll ◽  
Barbara F. Hecht
Keyword(s):  
Hearing Loss ◽  
Monitoring Program ◽  
Spoken Language ◽  
Position Statement ◽  
Outcome Data ◽  
Data Repository ◽  
Language Data ◽  
Children With Hearing Loss ◽  
Spoken Language Skills ◽  
Diverse Data

Purpose The purpose of the Listening and Spoken Language Data Repository (LSL-DR) was to address a critical need for a systemwide outcome data–monitoring program for the development of listening and spoken language skills in highly specialized educational programs for children with hearing loss highlighted in Goal 3b of the 2007 Joint Committee on Infant Hearing position statement supplement. Method The LSL-DR is a multicenter, international data repository for recording and tracking the demographics and longitudinal outcomes achieved by children who have hearing loss who are enrolled in private, specialized programs focused on supporting listening and spoken language development. Since 2010, annual speech-language-hearing outcomes have been prospectively obtained by qualified clinicians and teachers across 48 programs in 4 countries. Results The LSL-DR has been successfully implemented, bringing together the data collection efforts of these programs to create a large and diverse data repository of 5,748 children with hearing loss. Conclusion Due to the size and diversity of the population, the range of assessments entered, and the demographic information collected, the LSL-DR will provide an unparalleled opportunity to examine the factors that influence the development of listening in spoken language in this population.

A method for creation and validation of a natural spoken language corpus used for prosodic and speech perception

2003 ◽  
Vol 87 (1) ◽  
pp. 187 ◽  
Author(s):  
Beate Wendt ◽  
Klaus Hufnagel ◽  
André Brechmann ◽  
Birgit Gaschler-Markefski ◽  
Jürgen Tiedge ◽  
...  
Keyword(s):  
Speech Perception ◽  
Spoken Language ◽  
Language Corpus
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