Add to ORKGG ender and age estimation based on Gaussian Mixture Models (GMM) is introduced. Records from the Czech SpeechDat(E) database are used as training and test data set. In order to re-duce the data size, Mel-Frequency Cepstral Coefcients (MFCC) are extracted from the speech recordings. Maximum Likelihood (ML) training is applied to estimate the models ' parameters and additionaly discriminative training (DT) is applied to the trained models to provide further improvement of the results.
A novel gender classification system has been proposed based on Gaussian Mixture Models, which apply...
Speaker recognition is an ability to identify speaker’s characteristics based from spoken language. ...
In this paper, robust feature for Automatic text-independent Gender Identification System has been e...
This work deals with speaker´s age and gender recognition. At the beginning it introduces the practi...
DNN-HMMs, are recently very promising acoustic models achieving good speech recognition results over...
This paper compares two approaches of automatic age and gen-der classification with 7 classes. The f...
A recent trend in speech processing is embedding generation models, which can encode much of the rel...
Speech signals carry important information about a speaker such as age, gender, language, accent and...
This paper focuses on the automatic recognition of a per-son’s age and gender based only on his or h...
Extracting speaker-dependent paralinguistic information out of a person's voice, provides an opportu...
In this paper, we propose to use Gaussian mixture model (GMM) supervectors in a feed-forward deep ne...
Automatic age and gender recognition for speech applications is very important for a number of reaso...
Accurate gender classification is useful in speech and speaker recognition as well as speech emotion...
This paper presents a description of the INESC-ID Spoken Language Systems Laboratory (L2F) Age and G...
The emotional speech is considered as a prominent aspect in human speech communication. Emotional Sp...
A novel gender classification system has been proposed based on Gaussian Mixture Models, which apply...
Speaker recognition is an ability to identify speaker’s characteristics based from spoken language. ...
In this paper, robust feature for Automatic text-independent Gender Identification System has been e...
This work deals with speaker´s age and gender recognition. At the beginning it introduces the practi...
DNN-HMMs, are recently very promising acoustic models achieving good speech recognition results over...
This paper compares two approaches of automatic age and gen-der classification with 7 classes. The f...
A recent trend in speech processing is embedding generation models, which can encode much of the rel...
Speech signals carry important information about a speaker such as age, gender, language, accent and...
This paper focuses on the automatic recognition of a per-son’s age and gender based only on his or h...
Extracting speaker-dependent paralinguistic information out of a person's voice, provides an opportu...
In this paper, we propose to use Gaussian mixture model (GMM) supervectors in a feed-forward deep ne...
Automatic age and gender recognition for speech applications is very important for a number of reaso...
Accurate gender classification is useful in speech and speaker recognition as well as speech emotion...
This paper presents a description of the INESC-ID Spoken Language Systems Laboratory (L2F) Age and G...
The emotional speech is considered as a prominent aspect in human speech communication. Emotional Sp...
A novel gender classification system has been proposed based on Gaussian Mixture Models, which apply...
Speaker recognition is an ability to identify speaker’s characteristics based from spoken language. ...
In this paper, robust feature for Automatic text-independent Gender Identification System has been e...