Chapter 12. From pitch stylization to automatic tonal annotation of speech corpora

2019 ◽  
pp. 233-250
Author(s):  
Piet Mertens
Keyword(s):  
Speech Corpora

Rapid Collection of Spontaneous Speech Corpora Using Telephonic Community Forums

2018 ◽  
Author(s):  
Agha Ali Raza ◽  
Awais Athar ◽  
Shan Randhawa ◽  
Zain Tariq ◽  
Muhammad Bilal Saleem ◽  
...  
Keyword(s):  
Spontaneous Speech ◽  
Speech Corpora

scholarly journals Stimmen: A citizen science approach to minority language sociolinguistics

2021 ◽  
Vol 7 (s1) ◽  
Author(s):  
Nanna Haug Hilton
Keyword(s):  
Citizen Science ◽  
Smartphone Application ◽  
Minority Language ◽  
Phonological Variation ◽  
Methodological Issues ◽  
Speech Corpora ◽  
Societal Context ◽  
Smartphone Technology ◽  
Language Areas

Abstract This paper presents the project Stimmen fan Fryslân ‘Voices of Fryslân’. The project relies on a smartphone application developed to involve local communities in the creation of speech corpora, particularly of lesser used languages. This paper lays out the scientific and societal context of the project, showcases the smartphone application and gives an overview of the results from the project that attracted more than 15,000 users. Some key methodological issues are considered, and the paper discusses the role of smartphone technology for citizen science in minority language areas while also showing new maps with distributions of lexical and phonological variation in Frisian.

scholarly journals Analyzing dialect variation in historical speech corpora

2017 ◽  
Vol 142 (1) ◽  
pp. 406-421 ◽  
Author(s):  
Margaret E. L. Renwick ◽  
Rachel M. Olsen
Keyword(s):  
Speech Corpora ◽  
Dialect Variation

scholarly journals Joint optimization on decoding graphs using minimum classification error criterion

2014 ◽  
Vol 3 ◽  
Author(s):  
Abdelaziz A. Abdelhamid ◽  
Waleed H. Abdulla
Keyword(s):  
Likelihood Estimation ◽  
Discriminative Training ◽  
Language Models ◽  
Classification Error ◽  
Error Criterion ◽  
Speech Corpora ◽  
Finite State ◽  
Minimum Classification Error ◽  
Speech Features ◽  
Weighted Finite State Transducers

Motivated by the inherent correlation between the speech features and their lexical words, we propose in this paper a new framework for learning the parameters of the corresponding acoustic and language models jointly. The proposed framework is based on discriminative training of the models' parameters using minimum classification error criterion. To verify the effectiveness of the proposed framework, a set of four large decoding graphs is constructed using weighted finite-state transducers as a composition of two sets of context-dependent acoustic models and two sets of n-gram-based language models. The experimental results conducted on this set of decoding graphs validated the effectiveness of the proposed framework when compared with four baseline systems based on maximum likelihood estimation and separate discriminative training of acoustic and language models in benchmark testing of two speech corpora, namely TIMIT and RM1.

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