A Perspective Study on Speech Emotion Recognition: Databases, Features and Classification Models

Automatic Speech Recognition (ASR) is a popular research area with many variations in human behaviour functionalities and interactions. Human beings want speech for communication and Conversations. When the conversation is going on, the information or message of the speech utterances is transferred. It also consists of message which includes speaker’s traits like emotion, his or her physiological characteristics and environmental statistics. There is a tremendous number of signals or records that are complex and encoded, but these can be decoded quickly because of human intelligence. Many academics in the domain of Human Computer Interaction (HCI) are working to automate speech generation and the extraction of speech attributes and meaning. For example, ASR can regulate the usage of voice command and maintain dictation discipline while also recognizing and verifying the speech of the speaker. As a result of accent and nativity traits, the speaker's emotional state can be discerned from the speech. In this Paper, we discussed Speech Production System of Human, Research Problems in Speech Processing, SER system Motivation, Challenges and Objectives of Speech Emotion Recognition, so far the work done on Telugu Speech Emotion Databases and their role thoroughly explained. In this Paper, our own Created Database i.e., (DETL) Database for Emotions in Telugu Language and the software Audacity for creating that database is discussed clearly.

The role of inner speech in the speech production process

The article summarizes the current state of understanding of the concept of inner speech and evaluates the role of the internal language in the speech generation process. First, the available definitions of inner speech are presented and its features are briefly characterised. Subsequently, the inner voice is compared to overt speech and the main differences between those two planes of speech: the internal and the external one are outlined. Since the aim of the paper is to show the role of inner speech in overt speech production, a speech generation model which coalesces Levelt‘s (1993) assumptions with the stratifi cational approach to language is presented. Different stages of linguistic processing are described and the impact of internal languaging on linguistic output is discussed. It is claimed that inner speech plays a threefold role in overt speech production: (1) provides an inter-nal draft for external speech, (2) is vital for the self-monitoring system, and (3) supports working memory. Any impairment in the functioning of inner speech may thus lead to speech errors and slips of the tongue phenomena.

Gaya Bahasa dalam Karangan Teks Pidato Persuasif Siswa Generasi Z

Language style is a characteristic used by writers to convey meaning, thoughts, and feelings in written form so that it can be accepted by the readers. The purpose of this study is to describe the style of language in the persuasive speech text of generation Z students which includes a variety of affirmative language styles, language styles based on direct or indirect meaning, and language styles based on word choice. This study uses a qualitative approach with the type of research using text analysis research. The data analysis was carried out in 3 stages, there are data reduction, data presentation, and concluding. Based on the results of the research, 10 styles of affirmative language found described are repetition, pararima, alliteration, tautology, climax, anticlimax, rhetorical, polysyndeton, asindeton, and exclamation. The style of language based on whether or not the meaning is directly found is 6 rhetorical language styles are alliteration, assonance, asindeton, polysyndeton, pleonasm and tautology, and erothesis, and 2 figurative language styles are metonymy and paronomasia. The style of language based on the choice of words found are formal, informal, and conversation styles. Keywords: language style, persuasive speech, generation Z students Abstrak: Gaya bahasa merupakan ciri khas yang digunakan penulis untuk menyampaikan makna, pikiran dan perasaan dalam bentuk tulisan sehingga dapat diterima oleh pembaca. Tujuan penelitian ini untuk mendeskripsikan gaya bahasa dalam karangan teks pidato persuasif siswa generasi Z yang mencakup ragam gaya bahasa penegasan, gaya bahasa berdasarkan langsung tidaknya makna, dan gaya bahasa berdasarkan pilihan kata. Penelitian ini menggunakan pendekatan kualitatif dengan jenis penelitian analisis teks. Analisis data dilakukan dengan melalui 3 tahap yaitu reduksi data, penyajian data, dan penarikan simpulan. Berdasarkan hasil penelitian, dipaparkan ragam gaya bahasa penegasan yang ditemukan sebanyak 10 gaya bahasa yaitu repetisi, pararima, aliterasi, tautologi, klimaks, antiklimaks, retoris, polisindenton, asindenton, dan ekslamasio. Gaya bahasa berdasarkan langsung tidaknya makna yang ditemukan sebanyak 6 gaya bahasa retoris yaitu aliterasi, asonansi, asindenton, polisindenton, pleonasme dan tautologi, dan erotesis, dan 2 gaya bahasa kiasan yaitu metonimia dan paronomasia. Gaya bahasa berdasarkan pilihan kata yang ditemukan yaitu gaya bahasa resmi, tak resmi, dan percakapan. Kata kunci: gaya bahasa, pidato persuasif, siswa generasi Z

Conversation Engine for Deaf and Dumb

Deaf and hard hearing people use linguistic communication to exchange information between their own community and with others. Sign gesture acquisition and text/speech generation are parts of computer recognition of linguistic communication. Static and dynamic are classified as sign gestures. Both recognition systems are important to the human community but static gesture recognition is less complicated than dynamic gesture recognition. Inability to talk is taken into account to be a disability among people. To speak with others people with disability use different modes, there are number of methods available for his or her communication one such common method of communication is linguistic communication. Development of linguistic communication recognition application for deaf people is vital, as they’ll be able to communicate easily with even people who don’t understand language. Our project aims at taking the fundamental step in removing the communication gap between normal people, deaf and dumb people using language.

A Review on Machine Learning for Audio Applications

Audio processing is an important branch under the signal processing domain. It deals with the manipulation of the audio signals to achieve a task like filtering, data compression, speech processing, noise suppression, etc. which improves the quality of the audio signal. For applications such as natural language processing, speech generation, automatic speech recognition, the conventional algorithms aren’t sufficient. There is a need for machine learning or deep learning algorithms which can be implemented so that the audio signal processing can be achieved with good results and accuracy. In this paper, a review of the various algorithms used by researchers in the past has been described and gives the appropriate algorithm that can be used for the respective applications.

Synthetic speech detection through short-term and long-term prediction traces

Several methods for synthetic audio speech generation have been developed in the literature through the years. With the great technological advances brought by deep learning, many novel synthetic speech techniques achieving incredible realistic results have been recently proposed. As these methods generate convincing fake human voices, they can be used in a malicious way to negatively impact on today’s society (e.g., people impersonation, fake news spreading, opinion formation). For this reason, the ability of detecting whether a speech recording is synthetic or pristine is becoming an urgent necessity. In this work, we develop a synthetic speech detector. This takes as input an audio recording, extracts a series of hand-crafted features motivated by the speech-processing literature, and classify them in either closed-set or open-set. The proposed detector is validated on a publicly available dataset consisting of 17 synthetic speech generation algorithms ranging from old fashioned vocoders to modern deep learning solutions. Results show that the proposed method outperforms recently proposed detectors in the forensics literature.

Voice User Interface for the Neurodiversant

Technology for the neurodivergent hasembraced VUI in the form of several off theshelf cloud based technologies, replacingolder speech generation technology, in thispaper we use Amazon VUI as an exampleof accommodation engineering and digitalliteracy as we review the use of VUItechnologies and a move away from ABATherapy by the neurodivergent community.Keywords: VUI, Alexa, Echo,Neurodiversant, Neurotypical, DigitalLiteracy, Digital Divide.