Speech Recognition Using Historian Multimodal Approach
نویسندگان
چکیده
منابع مشابه
Toward Robust Multimodal Speech Recognition
In this paper, a robust multimodal speech recognition system is proposed in order to improve the performance of automatic speech recognition (ASR). A visual feature extraction technique for real-world data is developed and implemented. Multi-stream hidden Markov models (HMMs) including weighting factors are used to combine audio and visual information, applying the stream-weight optimization sc...
متن کاملMultimodal Human-Robot Interaction Using Gestures and Speech Recognition
This work proposes a Decision-Theoretic (DT) approach to problems involving interaction between robot systems and human users, which takes into account the latent aspects of Human-Robot Interaction (HRI), e.g., the user’s status. The presented approach is based on the Partially Observable Markov Decision Process (POMDP) framework, which efficiently handles uncertainty in planning problems invol...
متن کاملSpeech Representation Models for Speech Synthesis and Multimodal Speech Recognition
The field of speech recognition has seen steady advances over the last two decades, leading to the accurate, real-time recognition systems available on mobile phones today. In this thesis, I apply speech modeling techniques developed for recognition to two other speech problems: speech synthesis and multimodal speech recognition with images. In both problems, there is a need to learn a relation...
متن کاملSpeech Emotion Recognition Using Scalogram Based Deep Structure
Speech Emotion Recognition (SER) is an important part of speech-based Human-Computer Interface (HCI) applications. Previous SER methods rely on the extraction of features and training an appropriate classifier. However, most of those features can be affected by emotionally irrelevant factors such as gender, speaking styles and environment. Here, an SER method has been proposed based on a concat...
متن کاملMyoelectric signals for multimodal speech recognition
A Coupled Hidden Markov Model (CHMM) is proposed in this paper to perform multimodal speech recognition using myoeletric signals (MES) from the muscles of vocal articulation. MES signals are immune to noise, and words that are acoustically similar manifest distinctly in MES. Hence, they would effectively complement the acoustic data in a multimodal speech recognition system. Research in Audio-V...
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: The Egyptian Journal of Language Engineering
سال: 2019
ISSN: 2356-8216
DOI: 10.21608/ejle.2019.59164