Digital Assistant for Ventilators Using SVM Algorithm and Speech Recognition
نویسندگان
چکیده
منابع مشابه
Assistant-Based Speech Recognition for ATM Applications
—Situation awareness of today’s automation relies so far on sensor information, data bases and the information delivered by the operator using an appropriate user interface. Listening to the conversation of people is not addressed until today, but an asset in many working situations of teams. This paper shows that automatic speech recognition (ASR) integrating into air traffic management applic...
متن کاملSVM Scheme for Speech Emotion Recognition using MFCC Feature
Emotion recognition from speech has developed as a recent research area in Human–Computer Interaction. The objective of this paper is to use a 3-stage Support Vector Machine classifier to classify seven different emotions present in the Berlin Emotional Database. For the purpose of classification, MFCC features from all the 535 files present in the database are extracted. Nine statistical measu...
متن کاملHybrid SVM/HMM architectures for speech recognition
In this paper, we describe the use of a powerful machine learning scheme, Support Vector Machines (SVM), within the framework of hidden Markov model (HMM) based speech recognition. The hybrid SVM/HMM system has been developed based on our public domain toolkit. The hybrid system has been evaluated on the OGI Alphadigits corpus and performs at 11.6% WER, as compared to 12.7% with a triphone mixt...
متن کاملRobot Arm Performing Writing through Speech Recognition Using Dynamic Time Warping Algorithm
This paper aims to develop a writing robot by recognizing the speech signal from the user. The robot arm constructed mainly for the disabled people who can’t perform writing on their own. Here, dynamic time warping (DTW) algorithm is used to recognize the speech signal from the user. The action performed by the robot arm in the environment is done by reducing the redundancy which frequently fac...
متن کاملSvm-hmm Landmark Based Speech Recognition
Support vector machines (SVMs) are trained to detect acoustic-phonetic landmarks, and to identify both the manner and place of articulation of the phones producing each landmark with high accuracy. The discriminant outputs of these SVMs are used as input features for a standard HMM based ASR system. There is a significant improvement in both the phone and word recognition accuracy when using th...
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: International Research Journal on Advanced Science Hub
سال: 2020
ISSN: 2582-4376
DOI: 10.47392/irjash.2020.219