Age and gender recognition from speech signals
نویسندگان
چکیده
منابع مشابه
A Comparative Study of Gender and Age Classification in Speech Signals
Accurate gender classification is useful in speech and speaker recognition as well as speech emotion classification, because a better performance has been reported when separate acoustic models are employed for males and females. Gender classification is also apparent in face recognition, video summarization, human-robot interaction, etc. Although gender classification is rather mature in a...
متن کاملImproving automatic emotion recognition from speech signals
We present a speech signal driven emotion recognition system. Our system is trained and tested with the INTERSPEECH 2009 Emotion Challenge corpus, which includes spontaneous and emotionally rich recordings. The challenge includes classifier and feature sub-challenges with five-class and two-class classification problems. We investigate prosody related, spectral and HMM-based features for the ev...
متن کاملEmotion Recognition from Madarin Speech Signals
In this paper, a Mandarin speech based emotion classification method is presented. Five primary human emotions including anger, boredom, happiness, neutral and sadness are investigated. In emotion classification of speech signals, conventional features are statistics of fundamental frequency, loudness, duration and voice quality. However, the recognition accuracy of systems employing these feat...
متن کاملAge recognition based on speech signals using weights supervector
This paper proposes a new age-recognition system approach— building a Gaussian mixture model–based weights supervector features for a support vector machine (SVM). This approach uses the hypothesis that it is possible to find unique Gaussians for each age-group model in the universal background model (UBM). The weights of those Gaussians can lead to a discriminant way to separate the age groups...
متن کاملVoice-based Age and Gender Recognition using Training Generative Sparse Model
Abstract: Gender recognition and age detection are important problems in telephone speech processing to investigate the identity of an individual using voice characteristics. In this paper a new gender and age recognition system is introduced based on generative incoherent models learned using sparse non-negative matrix factorization and atom correction post-processing method. Similar to genera...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Physics: Conference Series
سال: 2019
ISSN: 1742-6588,1742-6596
DOI: 10.1088/1742-6596/1410/1/012073