Gender Based Emotion Recognition System for Telugu Rural Dialects Using Hidden Markov Models
نویسندگان
چکیده
Automatic emotion recognition in speech is a research area with a wide range of applications in human interactions. The basic mathematical tool used for emotion recognition is Pattern recognition which involves three operations, namely, pre-processing, feature extraction and classification. This paper introduces a procedure for emotion recognition using Hidden Markov Models (HMM), which is used to divide five emotional states: anger, surprise, happiness, sadness and neutral state. The approach is based on standard speech recognition technology using hidden continuous markov model by selection of low level features and the design of the recognition system. Emotional Speech Database from Telugu Rural Dialects of Andhra Pradesh (TRDAP) was designed using several speaker’s voices comprising the emotional states. The accuracy of recognizing five different emotions for both genders of classification is 80% for anger-emotion which is achieved by using the best combination of 39-dimensioanl feature vector for every frame (13 MFCCs, 13 Delta Coefficients and 13 Acceleration Coefficients) and a classifier using HMM. This outcome very much matches with that acquired with the same database with subjective evaluation by human judges. Both gender-dependent and gender-independent experiments are conducted on TRDAP emotional speech database.
منابع مشابه
Gender-dependent emotion recognition based on HMMs and SPHMMs
It is well known that emotion recognition performance is not ideal. The work of this research is devoted to improving emotion recognition performance by employing a two-stage recognizer that combines and integrates gender recognizer and emotion recognizer into one system. Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in the two-stage ...
متن کاملSpeaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words
متن کامل
Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words
متن کامل
Employing both gender and emotion cues to enhance speaker identification performance in emotional talking environments
Speaker recognition performance in emotional talking environments is not as high as it is in neutral talking environments. This work focuses on proposing, implementing, and evaluating a new approach to enhance the performance in emotional talking environments. The new proposed approach is based on identifying the unknown speaker using both his/her gender and emotion cues. Both Hidden Markov Mod...
متن کاملMAN-MACHINE INTERACTION SYSTEM FOR SUBJECT INDEPENDENT SIGN LANGUAGE RECOGNITION USING FUZZY HIDDEN MARKOV MODEL
Sign language recognition has spawned more and more interest in human–computer interaction society. The major challenge that SLR recognition faces now is developing methods that will scale well with increasing vocabulary size with a limited set of training data for the signer independent application. The automatic SLR based on hidden Markov models (HMMs) is very sensitive to gesture's shape inf...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/1006.4548 شماره
صفحات -
تاریخ انتشار 2010