Deep Neural Network Based Chinese Dialect Classification

Wang, Miao; Ren, Jie; Ma, Miao; Li, Zhiqiang; Rui, Cao; Gao, Quanli

doi:10.1109/cbd54617.2021.00043

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…MFCC (Davis & Mermelstein, 1980)는 저주파 대역에서의 오디 오 신호를 세밀하게 표현하고 고주파 대역에서는 상대적으로 소 략하게 표현하는 특성을 가진다. MFCC는 방언 분류 (Chowdhury et al, 2020;Khurana et al, 2017;Mukherjee et al, 2020;Tawaqal & Suyanto, 2021;Wan et al, 2022;Wang et al, 2021;Zhang & Hansen, 2018)뿐만 아니라 음성인식 (Shahnawazuddin et al, 2016;Tüske et al, 2014;Wallington et al, 2021), 화자 인식 (Fenu et al, 2020;Garcia-Romero et al, 2019;Lin & Mak, 2020;Pappagari et al, 2020) 그리고 감정 인식 (Keesing et al, 2021;Likitha et al, 2017;Sarma et al, 2018;Saste & Jagdale, 2017;Seo & Lee, 2022)과 같은 음성 분석의 다양한 분야에서도 활용된다.…”

Section: Mel-frequency Cepstral Coefficients(mfcc)unclassified

“…특성을 사용한 딥러닝 기반의 방법이 주류를 이루었다 (Chowdhury et al, 2020;Khurana et al, 2017;Mukherjee et al, 2020;Tawaqal & Suyanto, 2021;Wan et al, 2022;Wang et al, 2021;Zhang & Hansen, 2018). 최근에는 MFCC 특성에 운율학적 (Bhattacharjee & Sarmah, 2013;Lee et al, 2021Lee et al, , 2022Mehrabani & Hansen, 2015), 음성학적 (Chittaragi & Koolagudi, 2019;Najafian et al, 2018) 혹은 어휘적 (Michon et al, 2018;Ying et al, 2020) 특성을 결합하여 방언 분류 모델을 훈련시키는 연구가 많이 소개되고 있다.…”

unclassified

See 1 more Smart Citation

Dialect classification based on the speed and the pause of speech utterances*

Junyeong¹,

Lee²

2023

Phonetics Speech Sci.

View full text Add to dashboard Cite

In this paper, we propose an approach for dialect classification based on the speed and pause of speech utterances as well as the age and gender of the speakers. Dialect classification is one of the important techniques for speech analysis. For example, an accurate dialect classification model can potentially improve the performance of speaker or speech recognition. According to previous studies, research based on deep learning using Mel-Frequency Cepstral Coefficients (MFCC) features has been the dominant approach. We focus on the acoustic differences between regions and conduct dialect classification based on the extracted features derived from the differences. In this paper, we propose an approach of extracting underexplored additional features, namely the speed and the pauses of speech utterances along with the metadata including the age and the gender of the speakers. Experimental results show that our proposed approach results in higher accuracy, especially with the speech rate feature, compared to the method only using the MFCC features. The accuracy improved from 91.02% to 97.02% compared to the previous method that only used MFCC features, by incorporating all the proposed features in this paper.

show abstract

Section: Mel-frequency Cepstral Coefficients(mfcc)unclassified

unclassified

Dialect classification based on the speed and the pause of speech utterances*

Junyeong¹,

Lee²

2023

Phonetics Speech Sci.

View full text Add to dashboard Cite

show abstract

Chinese dialect speech recognition: a comprehensive survey

Li,

Mai,

Wang

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

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.

show abstract

Deep Neural Network Based Chinese Dialect Classification

Cited by 2 publications

References 0 publications

Dialect classification based on the speed and the pause of speech utterances*

Dialect classification based on the speed and the pause of speech utterances*

Chinese dialect speech recognition: a comprehensive survey

Contact Info

Product

Resources

About