Vocal tract area function extraction using ultrasound for articulatory speech synthesis

Mohapatra, Debasish Ray; Saha, Pramit; Liu, Yadong; Gick, Bryan; Fels, Sidney

doi:10.21437/ssw.2021-16

11th ISCA Speech Synthesis Workshop (SSW 11) 2021

DOI: 10.21437/ssw.2021-16

|View full text |Cite

Vocal tract area function extraction using ultrasound for articulatory speech synthesis

Debasish Ray Mohapatra¹,

Pramit Saha²,

Yadong Liu³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Improved Speaker Recognition System Using Automatic Lip Recognition

Kholiev,

Barkovska

2024

Control syst. comput.

View full text Add to dashboard Cite

The paper is focused on the relevant problem of speech recognition using additional sources besides the voice itself, in conditions in which the quality or availability of audio information is inadequate (for example, in the presence of noise or additional speakers). This is achieved by using automatic lip recognition (ARL) methods, which rely on non-acoustic biosignals generated by the human body during speech production. Among the applications of this approach are medical applications, as well as processing voice commands in languages with poor audio conditions. The aim of this work is to create a system for speech recognition based on a combination of speaker lip recognition (SSI) and context prediction. To achieve this goal, the following tasks were performed: to substantiate the systems for recognizing voice commands of a silent voice interface (SSI) based on a combination of two neural network architectures, to implement a model for recognizing visemes based on the CNN neural network architecture and an encoder-decoder architecture for the LSTM neural recurrent network model for analyzing and predicting the context of a speaker’s speech. The developed system was tested on a chosen dataset. The results show that the recognition error in different conditions averages from 4,34% to 5,12% for CER and from 5,52% to 6,06% for WER for the proposed ALR system in 7 experiments, which is an advantage over the LipNet project, which additionally processes audio data for the original without noise.

show abstract

Improved Speaker Recognition System Using Automatic Lip Recognition

Kholiev,

Barkovska

2024

Control syst. comput.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Vocal tract area function extraction using ultrasound for articulatory speech synthesis

Cited by 1 publication

References 0 publications

Improved Speaker Recognition System Using Automatic Lip Recognition

Improved Speaker Recognition System Using Automatic Lip Recognition

Contact Info

Product

Resources

About