RELEVANCE OF THE TYPES AND THE STATISTICAL PROPERTIES OF FEATURES IN THE RECOGNITION OF BASIC EMOTIONS IN SPEECH

Due to the advance of speech technologies and their increasing usage in various applications, automatic recognition of emotions in speech represents one of the emerging fields in human-computer interaction. This paper deals with several topics related to automatic emotional speech recognition, most notably with the improvement of recognition accuracy by lowering the dimensionality of the feature space and evaluation of the relevance of particular feature types. The research is focused on the classification of emotional speech into five basic emotional classes (anger, joy, fear, sadness and neutral speech) using a recorded corpus of emotional speech in Serbian

A Novel Approach to Density Near-Zero Acoustic Metamaterials

The study demonstrates the possibility of achieving near-zero propagation of sound waves in acoustic metamaterials based on a membrane-based metamaterial unit cell which exhibits effective mass density of Lorentzian type. The unit cell, which represents the acoustic counterpart of the split ring resonator, was previously used as a building block of left-handed metamaterials, as it exhibits negative density at certain frequencies. In this study we show that its application can be extended to achieving propagation of sound waves at a frequency where its effective density equals zero. This effect can be exploited in a range of applications where extremely low phase variation over long physical distances is required, such as energy tunneling or tailoring the acoustic radiation phase pattern in arbitrary ways. After discussing the dependence of the frequency response of the unit cell on the properties of the host, we show that it can be used to design near-zero acoustic filters with low insertion loss and steep roll-off. Finally, we show that it can be used to achieve simultaneous near-zero propagation at multiple, independently chosen frequencies

AUTOMATIC PROSODY GENERATION IN A TEXT-TO-SPEECH SYSTEM FOR HEBREW

The paper presents the module for automatic prosody generation within a system for automatic synthesis of high-quality speech based on arbitrary text in Hebrew. The high quality of synthesis is due to the high accuracy of automatic prosody generation, enabling the introduction of elements of natural sentence prosody of Hebrew. Automatic morphological annotation of text is based on the application of an expert algorithm relying on transformational rules. Syntactic-prosodic parsing is also rule based, while the generation of the acoustic representation of prosodic features is based on classification and regression trees. A tree structure generated during the training phase enables accurate prediction of the acoustic representatives of prosody, namely, durations of phonetic segments as well as temporal evolution of fundamental frequency and energy. Such an approach to automatic prosody generation has lead to an improvement in the quality of synthesized speech, as confirmed by listening tests

Cross-Lingual Neural Network Speech Synthesis Based on Multiple Embeddings

The paper presents a novel architecture and method for speech synthesis in multiple languages, in voices of multiple speakers and in multiple speaking styles, even in cases when speech from a particular speaker in the target language was not present in the training data. The method is based on the application of neural network embedding to combinations of speaker and style IDs, but also to phones in particular phonetic contexts, without any prior linguistic knowledge on their phonetic properties. This enables the network not only to efficiently capture similarities and differences between speakers and speaking styles, but to establish appropriate relationships between phones belonging to different languages, and ultimately to produce synthetic speech in the voice of a certain speaker in a language that he/she has never spoken. The validity of the proposed approach has been confirmed through experiments with models trained on speech corpora of American English and Mexican Spanish. It has also been shown that the proposed approach supports the use of neural vocoders, i.e. that they are able to produce synthesized speech of good quality even in languages that they were not trained on

Educational Applications for Blind and Partially Sighted Pupils Based on Speech Technologies for Serbian

The inclusion of persons with disabilities has always represented an important issue. Advancements within the field of computer science have enabled the development of different types of aids, which have significantly improved the quality of life of the disabled. However, for some disabilities, such as visual impairment, the purpose of these aids is to establish an alternative communication channel and thus overcome the user’s disability. Speech technologies play the crucial role in this process. This paper presents the ongoing efforts to create a set of educational applications based on speech technologies for Serbian for the early stages of education of blind and partially sighted children. Two educational applications dealing with memory exercises and comprehension of geometrical shapes are presented, along with the initial tests results obtained from research including visually impaired pupils

Speaker/Style-Dependent Neural Network Speech Synthesis Based on Speaker/Style Embedding

The paper presents a novel architecture and method for training neural networks to produce synthesized speech in a particular voice and speaking style, based on a small quantity of target speaker/style training data. The method is based on neural network embedding, i.e. mapping of discrete variables into continuous vectors in a low-dimensional space, which has been shown to be a very successful universal deep learning technique. In this particular case, different speaker/style combinations are mapped into different points in a low-dimensional space, which enables the network to capture the similarities and differences between speakers and speaking styles more efficiently. The initial model from which speaker/style adaptation was carried out was a multi-speaker/multi-style model based on 8.5 hours of American English speech data which corresponds to 16 different speaker/style combinations. The results of the experiments show that both versions of the obtained system, one using 10 minutes and the other as little as 30 seconds of target data, outperform the state of the art in parametric speaker/style-dependent speech synthesis. This opens a wide range of application of speaker/style dependent speech synthesis based on small quantities of training data, in domains ranging from customer interaction in call centers to robot-assisted medical therapy