Automatic gender identification optimised for language independence

Slomka, S.; Sridharan, Sridha

doi:10.1109/tencon.1997.647278

Cited by 17 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies were carried out such as the work of Slomka and Sridharan [17] which employed speakers (males and females) who speak 11 different languages. Then, they trained using these samples a linear classifier.…”

Section: A Audio Classificationmentioning

confidence: 99%

Spoken Arabic dialects identification: The case of Egyptian and Jordanian dialects

Al‐Ayyoub

Rihani

Dalgamoni

et al. 2014

2014 5th International Conference on Information and Communication Systems (ICICS)

View full text Add to dashboard Cite

Language identification systems have been the subject of extensive research efforts since the 1970's. Dialect identification is a natural extension of such systems. Most of the current systems do not consider the Arabic language and its dialects despite its immense importance. This paper addresses the problem of Arabic dialects identification. To the best of our knowledge, this is the first attempt to address this specific problem. Due to the lack of existing datasets, we collect our own dataset. To avoid collecting a prohibitively large dataset for the dozens of existing Arabic dialects, we focus on two specific dialects, viz. the Jordanian dialect and the Egyptian dialect. Several audio processing techniques (for feature extraction) and classifiers are tested to determine which combination of features/classifier generates the best results. Surprisingly, the results are very good.

show abstract

Section: A Audio Classificationmentioning

confidence: 99%

Spoken Arabic dialects identification: The case of Egyptian and Jordanian dialects

Al‐Ayyoub

Rihani

Dalgamoni

et al. 2014

2014 5th International Conference on Information and Communication Systems (ICICS)

View full text Add to dashboard Cite

show abstract

“…Feature extraction is often performed using gender related characteristics of speech such as pitch [1], [3], formant and harmonic structure [3], [4]. Other approaches rely on spectral features such as Mel-Frequency Cepstral or Spectral Coefficients (MFCC or MFSC) [2], [5], Linear Prediction Coefficients [6], Reflection Coefficients [6] and Log area Ratio Coefficients [5]. Classification techniques use Hidden Markov Models (HMM) [1], [4], Gaussian Mixture Models (GMM) [5], [7], [8] or Neural Networks [2].…”

Section: Introductionmentioning

confidence: 99%

“…Other approaches rely on spectral features such as Mel-Frequency Cepstral or Spectral Coefficients (MFCC or MFSC) [2], [5], Linear Prediction Coefficients [6], Reflection Coefficients [6] and Log area Ratio Coefficients [5]. Classification techniques use Hidden Markov Models (HMM) [1], [4], Gaussian Mixture Models (GMM) [5], [7], [8] or Neural Networks [2]. Multi-expert approaches have also been developed combining classification techniques [2], [5] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Practical Considerations for Real-Time Implementation of Speech-Based Gender Detection

Scheme

Castillo-Guerra

Englehart

et al. 2006

Lecture Notes in Computer Science

View full text Add to dashboard Cite

This paper describes a detailed analysis and implementation of a robust gender detector for audio stream applications. The implementation, based on melcepstral features and a Gaussian mixture model classifier, is designed to maximize gender classification performance in continuous speech. The described detector outperforms other reported systems based on statistically significant numbers of gender verifications (2136 unique speakers) obtained from the FISHER speech corpus. The system yields high accuracies for long and short utterances while a confidence figure of merit score for the decision ensures reliability in continuous audio streams.

show abstract

“…In their system Paris and Carey trained an GI system using speakers of British English and tested their system using speakers of British English, US English, and 10 other languages. Slomka and Sridharan proposed text-independent GI systems capable of being optimized for multiple adverse conditions, including various coders, and reverberation levels (Slomka, 1997).…”

Section: Introductionmentioning

confidence: 99%

Speaker’s Gender Identification for Human-Robot Interaction

Bae¹,

Kwak²,

Chi³

2006

Proceedings of the International Conference on Signal Processing and Multimedia Applications

View full text Add to dashboard Cite

This paper is concerned with a text-independent Speaker's gender Identification (GI) for Human-Robot Interaction (HRI). For this purpose, we perform speaker's gender recognition based on Gaussian Mixture Model (GMM) and use robot platform called WEVER, which is a Ubiquitous Robotic Companion (URC) intelligent service robot developed at Intelligent Robot Research Division in Electronics and Telecommunication Research Institute (ETRI). Furthermore, we communicate with intelligent service robots through a Korean-based spontaneous speech recognition and text-independent speaker's gender identification to provide a suitable service such as selection of preferable TV channel or music for the identified speaker's gender. The experimental results obtained for ETRI speaker database reveal that the approach presented in this paper yields a good identification (94.9%) performance within 3 meter.

show abstract

Automatic gender identification optimised for language independence

Cited by 17 publications

References 8 publications

Spoken Arabic dialects identification: The case of Egyptian and Jordanian dialects

Spoken Arabic dialects identification: The case of Egyptian and Jordanian dialects

Practical Considerations for Real-Time Implementation of Speech-Based Gender Detection

Speaker’s Gender Identification for Human-Robot Interaction

Contact Info

Product

Resources

About