Allophone-based acoustic modeling for Persian phoneme recognition

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Ahmadi Tahere; Karshenas Hossein; BABAALI BAGHER; Alinejad Batool

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: SIGNAL AND DATA PROCESSING Year:2020 | Volume:17 | Issue:3 (45) Page(s): 37-54

Download Full-Text

Persian Verion

View:

292

Download:

Cites:

Information Journal Paper

Title

Allophone-based acoustic modeling for Persian phoneme recognition

Author(s)

Ahmadi Tahere | Karshenas Hossein | BABAALI BAGHER | Alinejad Batool | Issue Writer Certificate

Keywords

automatic speech recognition

automatic phoneme recognition

context-dependent models

phoneme

allophone

coarticulation

Abstract

Phoneme recognition is one of the fundamental phases of automatic speech recognition. coarticulation which refers to the integration of sounds, is one of the important obstacles in phoneme recognition. In other words, each phone is influenced and changed by the characteristics of its neighbor phones, and coarticulation is responsible for most of these changes. The idea of modeling the effects of speech context, and using the context-dependent models in phoneme recognition is a method which used to compensate the negative effects of coarticulation. According to this method, if two similar phonemes in speech have different contexts, each of them constitute a separate model. In this research, a linguistic method called allophonic modeling has been used to model context effects in Persian phoneme recognition. For this purpose, in the first phase, the rules required for occurrence of various allophones of each phoneme, are extracted from Persian linguistic resources. So each phoneme is considered as a class, consisting of its various context-dependent forms named allophones. The necessary prerequisites for modeling and identifying allophones, is an allophonic corpus. Since there was no such corpus in Persian language, SMALL FARSDAT corpus has been used. This corpus is segmented and labelled manually for each sentence, word and phoneme. So the phonological and lingual context required for the realization of allophones, is implemented in this corpus. For example, the syllabification has been performed on the corpus and then, for each phoneme, its position (first, middle and end) in the word and syllable is specified using different numeric tags. In the next step, allophonic labeling has been performed by searching for each of the allophonic contexts in the corpus. These allophonic corpus is used to model and recognize the allophones of input speech. Finally, each allophone is assigned to a proper phonemic class so phoneme recognition has been done using allophones. The experimental results show a high accuracy of the proposed method in phenome recognition, indicating a significant improvement comparing with other state-of-the-art methods.

Cites

No record.

References

No record.

Cite

APA: Copy

Ahmadi, Tahere, Karshenas, Hossein, BABAALI, BAGHER, & Alinejad, Batool. (2020). Allophone-based acoustic modeling for Persian phoneme recognition. SIGNAL AND DATA PROCESSING, 17(3 (45) ), 37-54. SID. https://sid.ir/paper/961034/en

Vancouver: Copy

Ahmadi Tahere, Karshenas Hossein, BABAALI BAGHER, Alinejad Batool. Allophone-based acoustic modeling for Persian phoneme recognition. SIGNAL AND DATA PROCESSING[Internet]. 2020;17(3 (45) ):37-54. Available from: https://sid.ir/paper/961034/en

IEEE: Copy

Tahere Ahmadi, Hossein Karshenas, BAGHER BABAALI, and Batool Alinejad, “Allophone-based acoustic modeling for Persian phoneme recognition,” SIGNAL AND DATA PROCESSING, vol. 17, no. 3 (45) , pp. 37–54, 2020, [Online]. Available: https://sid.ir/paper/961034/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops