Using Prosodic and Acoustic Features for Chinese Dialects Identification

Sun, Liguo

doi:10.1145/3421558.3421577

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Naturally, Chinese dialects have also become the focus of concern, for instance, the popularity of specific dialect tones and vocabulary. Sun (2020) proposed a prosodic-acoustic topic model, which verified that the acoustic features of Chinese dialects could be obtained through the unsupervised learning method. List (2015) pointed out that Chinese dialects can be regarded as a whole and promote each other with Mandarin.…”

Section: Phonetic Characteristics Of Chinese Dialectsmentioning

confidence: 79%

Chinese dialect speech recognition: a comprehensive survey

Li,

Mai,

Wang

et al. 2024

Artif Intell Rev

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As a multi-ethnic country with a large population, China is endowed with diverse dialects, which brings considerable challenges to speech recognition work. In fact, due to geographical location, population migration, and other factors, the research progress and practical application of Chinese dialect speech recognition are currently at different stages. Therefore, exploring the significant regional heterogeneities in specific recognition approaches and effects, dialect corpus, and other resources is of vital importance for Chinese speech recognition work. Based on this, we first start with the regional classification of dialects and analyze the pivotal acoustic characteristics of dialects, including specific vowels and tones patterns. Secondly, we comprehensively summarize the existing dialect phonetic corpus in China, which is of some assistance in exploring the general construction methods of dialect phonetic corpus. Moreover, we expound on the general process of dialect recognition. Several critical dialect recognition approaches are summarized and introduced in detail, especially the hybrid method of Artificial Neural Network (ANN) combined with the Hidden Markov Model(HMM), as well as the End-to-End (E2E). Thirdly, through the in-depth comparison of their principles, merits, disadvantages, and recognition performance for different dialects, the development trends and challenges in dialect recognition in the future are pointed out. Finally, some application examples of dialect speech recognition are collected and discussed.

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Section: Phonetic Characteristics Of Chinese Dialectsmentioning

confidence: 79%

Chinese dialect speech recognition: a comprehensive survey

Li,

Mai,

Wang

et al. 2024

Artif Intell Rev

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“…Although research on the automatic detection of diseases based on speech is rapidly expanding, it faces a number of challenges in terms of algorithm generalisability and potential application in real-world scenarios. These challenges include gender and age distribution, the presence of different mother tongues, dialects, sociolects, or cognitive aspects such as individual speech-language and reading competence that may affect various acoustic parameters (Alves et al, 2015; Goyal et al, 2021; Nagumo et al, 2020; Procter & Joshi, 2020; Rojas et al, 2020; Sun, 2020; Taylor et al, 2020). Studies on COVID-19 face additional challenges related to the fact that COVID-19 is a relatively new and not yet well understood disease with a wide range of symptoms and divergent symptom severity (Hu et al, 2020; Tu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

The Acoustic Dissection of Cough: Diving into Machine Listening-based COVID-19 Analysis and Detection

Ren

Chang

Bartl-Pokorny

et al. 2022

Preprint

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Purpose: The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19's transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge on the acoustic characteristics of COVID-19 cough sounds is limited, but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds. Methods: With the theory of computational paralinguistics, we analyse the acoustic correlates of COVID-19 cough sounds based on the COMPARE feature set, i. e., a standardised set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions. Results: The experimental results demonstrate that a set of acoustic parameters of cough sounds, e. g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, are relevant for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our automatic COVID-19 detection model performs significantly above chance level, i. e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positive/negative: 210/1,201). Conclusions: Based on the acoustic correlates analysis on the COMPARE feature set and the feature analysis in the effective COVID-19 detection model, we find that the machine learning method to a certain extent relies on acoustic features showing higher effects in conventional group difference testing.

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“…These challenges include gender and age distribution, the presence of different mother tongues, dialects, sociolects, or cognitive aspects such as individual speech-language and reading competence that may affect various acoustic parameters. 30 , 31 , 32 , 33 , 34 , 35 , 36 Studies on COVID-19 face additional challenges related to the fact that COVID-19 is a relatively new and not yet well understood disease with a wide range of symptoms and divergent symptom severity. 37 , 38 Studies need to consider the symptom heterogeneity of COVID-19 positive patients and the fact that many symptoms may also occur in other diseases such as bronchial asthma or flu.…”

Section: Introductionmentioning

confidence: 99%