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One service provided by our application ‘Speech Assistant System’ assisting the teaching of the hearing impaired to speak is the automatic assessment of words and sentences in the course of practice and feedback to the person. Individual speech sounds can only be correctly evaluated if they are compared with the appropriate reference speech sounds. This requires segmenting the speech to be examined...
This paper proposes the rehabilitation treatment coach robot which will help at-home patients do their rehabilitation exercises at home without any professional trainers. The coach robot is designed to be cheap enough for patients to afford it. The robot suggests the rehabilitation program and corrects the posture of the patients during the exercise. The deep neural network is used for posture correction...
In this paper, efficiency comparison of Support Vector Machines (SVM) and Binary Support Vector Machines (BSVM) techniques in utterance-based emotion recognition is studied. Acoustic features including energy, Mel-frequency cepstral coefficients (MFCC), Perceptual linear predictive (PLP), Filter bank (FBANK), pitch, their first and second derivatives are used as frame-based features. Four basic emotions...
In a real-life scenario, the acoustic characteristics of speech often suffer from the variations induced by diverse environmental noises and different speakers. To overcome the speaker-related speech variation problem for Automatic Speech Recognition (ASR), many speaker adaptation techniques have been proposed and studied. Almost all of these studies, however, only considered the speakers' long-term...
Biometric security systems based on predefined speech sentences are extremely common nowadays, particularly in low-cost applications where the simplicity of the hardware involved is a great advantage. Audio spoofing verification is the problem of detecting whether a speech segment acquired from such a system is genuine, or whether it was synthesized or modified by a computer in order to make it sound...
Symbolic reasoning is difficult for neural networks. Especially, reasoning with variables can be a challenging task for them. In this paper, a symbolic reasoning method based on deep neural networks is proposed, and this method is applied to axiom discovery. This method makes use of the concept of “symbolic manipulation”. Specifically, it relies on the learning ability of the deep neural networks...
Good speaker recognition systems should identify the speaker irrespective of what is spoken, including non-speech sounds that are often produced during natural conversations. In this work, the inclusion of breath sounds in the training phase of the speaker recognition is analyzed using the popular Gaussian mixture model-universal background model (GMM-UBM) and deep neural network (DNN) based systems...
Communication through voice is one of the main components of affective computing in human-computer interaction. In this type of interaction, properly comprehending the meanings of the words or the linguistic category and recognizing the emotion included in the speech is essential for enhancing the performance. In order to model the emotional state, the speech waves are utilized, which bear signals...
With the rapid advancement in technology, we still observe a significant amount of deaths of children under the age of five years. Majority of these deaths worldwide can be attributed to various medical conditions out of which three are very significant: birth asphyxia, preterm and infections. Birth asphyxia (perinatal asphyxia) is a medical condition which is characterised by abnormal breathing patterns...
This paper presents the implementation of a practical voice recognition system using MATLAB (R2014b) to secure a given user's system so that only the user may access it. Voice recognition systems have two phases, training and testing. During the training phase, the characteristic features of the speaker are extracted from the speech signal and stored in a database. In the testing phase, the stored...
Introducing features that better represent the visual information of speakers during the speech production is still an open issue that highly affects the quality of the lip-reading and Audio Visual Speech Recognition (AVSR) tasks. In this paper, three different types of visual features from both the image-based and model-based ones are investigated inside a professional lip reading task. The simple...
The phoneme set influence for Lithuanian speech commands recognition accuracy is investigated. Four phoneme sets are discussed. LIEPA speech corpus for training of Acoustic Model is used. The phonetic representation of corpus transcriptions is generated by grapheme-to-phoneme transformation rules. Rule based transformations for Lithuanian language is proposed. Recognition engine with CMU Pocketsphinx...
In speech recognition system, an improved multi-base neural network speech recognition model is proposed to solve the problem of long learning time and slow convergence rate of deep neural network. However, the improved model introduces a large number of parameters in the training process to make the model over-fitted in the test set, resulting in the deterioration of generalization ability and the...
Most traditional template matching based keyword recognition methods don't need training data, just rely on frame matching. However, the recognition speed is relatively slow and it can't be used in practice. The LVCSR-based method needs to convert the speech signal into text signal before recognition, which has an important impact on the final recognition performance. In this paper, we propose a method...
Significant developments in deep learning methods have been achieved with the capability to train more deeper networks. The performance of speech recognition system has been greatly improved by the use of deep learning techniques. Most of the developments in deep learning are associated with the development of new activation functions and the corresponding initializations. The development of Rectified...
This paper presents our work on developing acoustic models using deep neural networks (DNN) for low resource languages. This is considered one of the challenging problems in automatic speech recognition (ASR) as DNNs need large amount of data for building efficient models. The techniques explored in this approach use a common idea of transferring knowledge from models of high resource language to...
For the problem low speech recognition rate, an improved method of combining Deep Belief Network (DBN) with support vector machine (SVM) for analyzing Small sample speech signals is proposed. The speech signal data collected as the training sample is used for training the DBN to get the optimal parameter values. The trained DBN is utilized for feature extraction, and these speech sample data signals...
Traditional speech-related identity recognition commonly pays attention to individual aspect of speech signals but in reality, the speech signals are made up of semantics, speaker dependent features, etc. This paper therefore presents a new study that recognizes simultaneously multidimensional speaker information. In order to extract sufficient relational features, both high-level and low-level features...
In this paper, we investigate various training methods for building deep neural network (DNN) based acoustic models for dysarthric speech data. Methods like multitask learning, knowledge distillation and model adaptation, which overcome data sparsity and model over-fitting problems are employed to study the merits of each method. In Knowledge distillation framework, some privilege information in addition...
Automatic speech recognition can be used to evaluate the accuracy of read speech and thus serve a valuable role in literacy development by providing the needed feedback on reading skills in the absence of qualified teachers. Given the known limitations of ASR in the face of insufficient task-specific training data, the selection of acoustic and language modeling strategies can play a crucial role...
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