English-Chinese bilingual text-independent speaker verification

Ma, Bin; Meng, Helen

doi:10.1109/icassp.2004.1327105

Cited by 9 publications

(3 citation statements)

References 5 publications

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“…Cross-lingual speaker recognition has been in focus for researchers for some time because of the abundance of bilingual speakers in the world. Ma and Meng (2004) studied the enrollment-test mismatch and found that it caused significant performance degradation for speaker recognition. Auckenthaler et al (2001) investigated the mismatch between training and operation, within a GMM-UBM architecture, finding considerable performance degradation if the speech data used to train the Universal Background Model and the data used to validate/test speakers were in different languages.…”

Section: Related Workmentioning

confidence: 99%

Cross-Lingual Speaker Identification for Indian Languages

Rizvi,

Jamatia,

Rudrapal

et al. 2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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The paper introduces a cross-lingual speaker identification system for Indian languages, utilising a Long Short-Term Memory dense neural network (LSTM-DNN). The system was trained on audio recordings in English and evaluated on data from Hindi, Kannada, Malayalam, Tamil, and Telugu, with a view to how factors such as phonetic similarity and native accent affect performance. The model was fed with MFCC (mel-frequency cepstral coefficient) features extracted from the audio file. For comparison, the corresponding melspectrogram images were also used as input to a ResNet-50 model, while the raw audio was used to train a Siamese network. The LSTM-DNN model outperformed the other two models as well as two more traditional baseline speaker identification models, showing that deep learning models are superior to probabilistic models for capturing low-level speech features and learning speaker characteristics.

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Section: Related Workmentioning

confidence: 99%

Cross-Lingual Speaker Identification for Indian Languages

Rizvi,

Jamatia,

Rudrapal

et al. 2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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show abstract

“…In (Ma and Meng, 2004), bilingual text-independent speaker recognition task was studied where each speaker is trained using English data and tested with Chinese data. In that study, it was reported that language mismatch between training and test data yields significant degradation.…”

Section: Sectionmentioning

confidence: 99%

“…In that study, it was reported that language mismatch between training and test data yields significant degradation. To alleviate this degradation, authors proposed to model each speaker using both languages (Ma and Meng, 2004). Another solution for bilingual speaker recognition is training two separate speaker models for each target speaker one with Spanish data and the other using English data (Akbacak and Hansen, 2007).…”

Section: Sectionmentioning

confidence: 99%

Türkçe Konuşmaci Doğrulamada Di̇l Uyumsuzluğunun Etki̇si̇

Hanilçi¹

2017

Uludağ University Journal of the Faculty of Engineering

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Abstract:In this paper, effect of language mismatch between background data and evaluation data is analyzed for text-independent speaker recognition in particular for Turkish spoken language. Gaussian mixture model with universal background model (GMM-UBM) classifier is utilized using Mel-frequency cepstral coefficients (MFCCs) as speaker-specific features. Experiments conducted on a Turkish speech database consisting of 47 male and 26 female speakers reveals that Turkish speaker recognition performance dramatically degrades in case of language mismatch between UBM and the evaluation data. For example 1.73% and 12.34% equal error rates (EERs) are obtained for male speakers when UBM is trained using Turkish and English data, respectively. Keywords: Turkish speaker recognition, language mismatch Türkçe Konuşmacı Doğrulamada Dil Uyumsuzluğunun EtkisiÖz: Bu çalışmada, arkaplan verisi ile gerçekleştirme verisi arasında konuşulan dil anlamında bir uyumsuzluk olması durumunda Türkçe konuşmalar için konuşmacı tanıma performansı incelenmiştir. Gauss karışım modeli -genel arkaplan modeli sınıflandırıcısı ile mel-frekansı kepstral katsayıları konuşmacılara özgü öznitelikler olarak seçilmiştir. 47 erkek ve 26 bayan konuşmacıdan oluşan Türkçe veritabanı ile yapılan deneylerde görülmüştür ki arkaplan modelini eğitmek için kullanılan seslerin dili ile konuşmacı doğrulama deneylerinde kullanılan dil farklı olduğunda konuşmacı doğrulama performansı dramatik bir şekilde düşmektedir. Örneğin, erkek konuşmacılar için Türkçe ses verileri ile arkaplan modeli eğitildiğinde %1.73 eşit hata oranı elde edilirken, İngilizce sesler ile eğitildiğinde %12.34 eşit hata oranı elde edilmiştir.Anahtar Kelimeler: Türkçe konuşmacı doğrulama, dil uyumsuzluğu.

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An overview of robustness related issues in speaker recognition

Zheng¹,

Jin

Li³

et al. 2014

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific

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Speaker recognition technologies have been improved rapidly in recent years. However, critical robustness issues need to be addressed when they are applied in practical situations. This paper provides an overview of technologies dealing with robustness related issues in automatic speaker recognition. We first categorize the robustness issues into three categories, including environment-related, speaker-related and application-oriented issues. For each category, we then describe the current hot topics, existing technologies, and potential research focuses in the future.I.

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English-Chinese bilingual text-independent speaker verification

Cited by 9 publications

References 5 publications

Cross-Lingual Speaker Identification for Indian Languages

Cross-Lingual Speaker Identification for Indian Languages

Türkçe Konuşmaci Doğrulamada Di̇l Uyumsuzluğunun Etki̇si̇

An overview of robustness related issues in speaker recognition

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