Automatic transcription of continuous speech using unsupervised and incremental training

Ghadiyaram, G.L. Sarada; Nagarajan, N.; Thangavelu, Thyagarajan; Murthy, Hema A.

doi:10.21437/interspeech.2004-169

Interspeech 2004 2004

DOI: 10.21437/interspeech.2004-169

|View full text |Cite

Automatic transcription of continuous speech using unsupervised and incremental training

G.L. Sarada Ghadiyaram¹,

N. Nagarajan²,

Thyagarajan Thangavelu³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2006

2016

Publication Types

Select...

Other5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 6 publications

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Combination of system and source characteristics for speaker verification under limited data condition

Kumari

Jayanna²

2016

2016 IEEE 12th International Colloquium on Signal Processing &Amp; Its Applications (CSPA)

View full text Add to dashboard Cite

There is immense potential for speaker verification system under limited data condition in several real life applications. This paper explains how the combined dissimilar characteristics of voice data improve the performance of speaker verification when training and testing data lengths are reduced (less than 15 sec). To carry out this work, Mel-Frequency Cepstral Coefficients (MFCC), Linear Prediction Cepstral Coefficient (LPCC), Linear Prediction Residual (LPR) and Linear Prediction ResidualPhase (LPRP) features are considered. The features are extracted from these extraction techniques are studied individually and pooled them to acquire better verification performance. The experimental evaluation is made by different classifiers of Gaussian mixture model (GMM) and GMM-universal background model (GMM-UBM). The NIST-2003 dataset is used for conducting experiments. The combined dissimilar features provide relatively improved performance compared to all individual features. The GMM-UBM classifier comparatively gives reduced equal error rate (EER) compared to GMM.

show abstract

Combination of system and source characteristics for speaker verification under limited data condition

Kumari

Jayanna²

2016

2016 IEEE 12th International Colloquium on Signal Processing &Amp; Its Applications (CSPA)

View full text Add to dashboard Cite

show abstract

Limited data speaker verification using MFSR analysis technique

Kumari

Jayanna

2015

2015 International Conference on Trends in Automation, Communications and Computing Technology (I-Tact-15)

View full text Add to dashboard Cite

In this paper, the task of verifying the speaker using limited training and testing data is addressed. Speaker verification under limited data may not be able to produce sufficient feature vector for training and testing. This creates poor speaker modelling during training and testing. To defeat this problem, feature vectors for training and testing are increased. To increase the feature vectors, multiple frame size (MFS), multiple frame rate (MFR) and multiple frame size and rate (MFSR) analysis techniques are explored. These techniques comparatively increase more feature vectors during training and testing compared to single frame size and rate (SFSR) analysis. With the help of these feature vectors improved modeling and testing can be done under limited data. To demonstrate this we have used Mel-frequency cepstral coefficients (MFCC) as feature extraction technique. Gaussian mixture modelling-Universal background model (GMM-UBM) is used for modelling the speaker. The NIST-2003 is used as database for conducting the experiments. The experimental results show that there is an improvement in the performance of speaker verification if we use MFS, MFR and MFSR are analysis techniques instead of SFSR.Further, the experimental results show that MFSR gives improved performance over other analysis techniques.

show abstract

Multiple frame size and multiple frame rate feature extraction for speech recognition

Sarada

Nagarajan

Murthy

2004 International Conference on Signal Processing and Communications, 2004. SPCOM '04.

View full text Add to dashboard Cite

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.

Automatic transcription of continuous speech using unsupervised and incremental training

Cited by 6 publications

References 6 publications

Combination of system and source characteristics for speaker verification under limited data condition

Combination of system and source characteristics for speaker verification under limited data condition

Limited data speaker verification using MFSR analysis technique

Multiple frame size and multiple frame rate feature extraction for speech recognition

Contact Info

Product

Resources

About