Development of a Thai phonetically balanced monosyllabic word recognition test: Derivation of phoneme distribution, word list construction, and response evaluations

Tantibundhit, Charturong; Onsuwan, Chutamanee; Munthuli, Adirek; Sirimujalin, Ploypailin; Anansiripinyo, Thanaporn; Phuechpanpaisal, Sutanya; Wright, Nida; Kosawat, Krit

doi:10.1016/j.specom.2018.07.004

Cited by 4 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Torres et al [332] worked on the NIST 2016 SRE system which included Thai as one of the languages in the dataset. Tantibundhit et al [321] developed a Thai speech recognition system based on different factors, namely, phonemic balance, familiarity, reliability, list equivalency, and homogeneity. The limitations of the system were also discussed.…”

Section: Thaimentioning

confidence: 99%

Computational intelligence in processing of speech acoustics: a survey

Singh

Kaur

Kukreja

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

Speech recognition of a language is a key area in the field of pattern recognition. This paper presents a comprehensive survey on the speech recognition techniques for non-Indian and Indian languages, and compiled some of the computational models used for processing speech acoustics. An immense number of frameworks are available for speech processing and recognition for languages persisting around the globe. However, a limited number of automatic speech recognition systems are available for commercial use. The gap between the languages being spoken around the globe and the technical support available to these languages are very few. This paper examined major challenges for speech recognition for different languages. Analysis of the literature shows that lack of standard databases availability of minority languages hinder the research recognition research across the globe. When compared with non-Indian languages, the research on speech recognition of Indian languages (except Hindi) has not achieved the expected milestone yet. Combination of MFCC and DNN–HMM classifier is most commonly used system for developing ASR minority languages, whereas in some of the majority languages, researchers are using much advance algorithms of DNN. It has also been observed that the research in this field is quite thin and still more research needs to be carried out, particularly in the case of minority languages.

show abstract

Section: Thaimentioning

confidence: 99%

Computational intelligence in processing of speech acoustics: a survey

Singh

Kaur

Kukreja

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

Validating Four Hmong Word Recognition Tests With Normal-Hearing Bilingual Hmong Individuals

et al. 2022

View full text Add to dashboard Cite

Purpose: The purpose of this study was to validate four digitally recorded and phonetically balanced 50-word recognition lists in the White Hmong dialect with normal-hearing bilingual Hmong adults. Method: Using a randomized, incomplete-block design, each participant listened to and repeated four unique Hmong lists delivered by a female and a male talker. Participants were also tested with an English word list—List 1A of the Northwestern University Auditory Test No. 6. Participants' correct pronunciation of each word was scored. A nonparametric Mann–Whitney U Location Difference Test for Equivalence using two one-sided tests equivalence hypothesis: −0.02 < [(List_1) – (List_2)] < 0.02 was conducted to assess equivalence among all four Hmong and the English lists. Results: Seventy Hmong speakers participated in this study (35 women, 35 men; M age = 29.5 years, SD = 7.1). In all four Hmong lists, 93.5% (187/200) words met the validation criteria for ≥ 92% correct pronunciation. The 13 difficult words were deemed adequate by a Hmong panel and, therefore, were included to maintain four unique, balanced word lists. The test revealed that the Hmong and English word lists were considered equivalent at the 2% bound. Conclusion: The four Hmong word lists were validated to ensure an equal range of word difficulty across the lists.

show abstract

Evaluating Hearing Status and Word Recognition Ability in the Hmong Population Using Four Validated Monosyllabic White Hmong Dialect Word Recognition Tests

Lor,

O'Donnell,

Brown

et al. 2024

Am J Audiol

View full text Add to dashboard Cite

Purpose: The study's aims were (a) to evaluate hearing status and (b) word recognition ability of Hmong speakers using four validated monosyllabic word recognition tests in the White Hmong dialect and (c) to assess the relationship between the participant's language and the average word recognition percent correct scores, adjusting for age, gender, and degree of hearing loss. Method: Participants listened to two randomly assigned validated Hmong word lists (male/female talker) for each ear. Pure-tone air- and bone-conduction thresholds as well as word recognition ability were measured. Descriptive statistics were calculated to analyze the percent correct of word lists and classify hearing status. A nonparametric regression analysis was used to assess the relationship between the participant's language and the average word recognition percent correct scores, adjusting for age, gender, and degree of hearing loss. Results: Forty-eight Hmong (25 females, 23 males; M age = 44.4) participated in this study. Thirty-three participants had hearing loss in at least one ear, and 15 had hearing within normal limits bilaterally. Participants with normal, mild, or steeply sloping hearing loss reached an average word recognition score of > 94% on Hmong lists by both male and female talkers. Participants with moderate-to-severe hearing loss scored 68% on average for the male talker Hmong lists and 60% on average for the female talker Hmong lists. Gender was significantly positively associated with average word recognition percent correct on the female word lists ( b = −0.224, p = .047) but not statistically significant for the male word lists ( b = 7.579, p = .141). Conclusion: Findings provide support for the use of the four Hmong word lists in clinical settings.

show abstract

Development of a Thai phonetically balanced monosyllabic word recognition test: Derivation of phoneme distribution, word list construction, and response evaluations

Cited by 4 publications

References 8 publications

Computational intelligence in processing of speech acoustics: a survey

Computational intelligence in processing of speech acoustics: a survey

Validating Four Hmong Word Recognition Tests With Normal-Hearing Bilingual Hmong Individuals

Evaluating Hearing Status and Word Recognition Ability in the Hmong Population Using Four Validated Monosyllabic White Hmong Dialect Word Recognition Tests

Contact Info

Product

Resources

About