There are several types of Speech Recognition and Object Detection models available so far. This model is developed after undergoing the literature survey and the existing models that are related to Object Detection and Speech Recognition. The goal of this paper is to develop a model which is the integrated version of both SpeechRecognition and Object detection. KeywordsConnectionist approach–Explicit duration modeling–Discriminative training–Margin based estimation–HMM limitations Tied mixture modeling, and handling of distant speech signals are analyzed along with the directions for future research. Further, various challenges and performance issues such as environmental variability, Training and margin based estimation methods. The approaches emphasized in this part of review are connectionist approach, explicit duration modeling, discriminative Current advancements related to this topic areĪlso outlined. This paper (Part II) presents a review on the techniques which have been proposed in literatureįor the refinements of standard HMM methods to cope with their limitations. As discussed in Part I, the conventional acoustic models used for ASR have many drawbacks like weak duration
In automatic speech recognition (ASR) systems, hidden Markov models (HMMs) have been widely used for modeling the temporal
By applying these measures, this study is to unveil the procedures where multi-language speech-to-speech translation system has been successfully developed for mobile devices. After implementing the actual services, the massive database collected through the service was additionally applied to the system following a filtering process in order to procure the best-possible robustness toward both the details and the environment of the users' utterances. Moreover, with the speech-to-speech translation UI, a user-friendly UI has been designed and at the same time, errors were reduced during the process of translation as many measures to enhance user satisfaction were employed. Through this study, it was made possible to secure excellent basic performance under the environment similar to speech-to-speech translation environment, rather than just under the experimental environment. This study has established a massive language and speech database closest to the environment where speech-to- speech translation device actually is being used after mobilizing plenty of people based on the survey on users' demands. In order to develop a speech-to-speech translation system that can be widely used by many users, however, the system needs to reflect various characteristics of utterances by the users who are actually to use the speech-to-speech translation system other than improving the basic functions under the experimental environment. This application will add a brand-new era of HCI implementation for educational purposes.Īlong with the advancement of speech recognition technology and machine translation technology in addition to the fast distribution of mobile devices, speech-to-speech translation technology no longer remains as a subject of research as it has become popularized throughout many users. The proposed system utilizes a webcam as an input device. The system will allow the user to manage cursor, keyboard, and writing functions using hand gestures, and also provides the user with an additional feature to convert the user’s voice to text. This paper proposes a virtually controlled system that uses hand gestures and voice to perform operations. The gesture is made by the user is detected by the machine via the image processing techniques and the operation unique to the machine is carried out, thereby minimizing the requirement of any hardware input device. Typing has taken many forms, first using a keyboard, gradually changing to touch screens, and now too much easier finger motion tracking systems. Overcoming this difficulty stands as the ultimate goal for our proposed system. Amateur or aged people find it hard to identify and press the exact alphabet that they need. Identifying and interpreting hand gestures from a continuous sequential stream of input data is called gesture recognition. Hand Gesture-based communication is one of the most effortless and natural methods. Hand gesture recognition technology brought a brand-new era to the artificial intelligence branch of human-computer interaction. Human-Computer Interaction (HCI) grew enormously over the years.
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