Most Recent Oracle 1Z0-1122-23 Exam Questions & Answers

Text to speech (TTS) is an AI task that involves audio generation from text. TTS is a technology that converts text into spoken audio using natural sounding voices. TTS can read aloud any text data, such as PDFs, websites, books, emails, etc., and provide an auditory format for accessing written content. TTS can be helpful for anyone who needs to listen to text data for various reasons, such as accessibility, convenience, multitasking, learning, entertainment, etc. TTS uses different techniques and models to generate speech from text data, such as:

Concatenative synthesis: Combining pre-recorded segments of human speech based on the phonetic units of the text.

Parametric synthesis: Generating speech signals from acoustic parameters derived from the text using statistical models.

Neural synthesis: Using deep neural networks to learn the mapping between text and speech features and produce high-quality speech signals.

Expressive synthesis: Adding emotions or styles to the speech output to make it more natural and engaging.Reference::Text-to-Speech AI: Lifelike Speech Synthesis | Google Cloud,Text-to-speech synthesis - Wikipedia

Natural Language Processing (NLP) is an AI domain that is associated with tasks such as identifying the sentiment of text and translating text between languages. NLP is an interdisciplinary field that combines computer science, linguistics, and artificial intelligence to enable computers to process and understand natural language data, such as text or speech. NLP involves various techniques and applications, such as:

Text analysis: Extracting meaningful information from text data, such as keywords, entities, topics, sentiments, emotions, etc.

Text generation: Producing natural language text from structured or unstructured data, such as summaries, captions, headlines, stories, etc.

Machine translation: Translating text or speech from one language to another automatically and accurately.

Question answering: Retrieving relevant answers to natural language questions from a knowledge base or a document collection.

Speech recognition: Converting speech signals into text or commands.

Speech synthesis: Converting text into speech signals with natural sounding voices.

Natural language understanding: Interpreting the meaning and intent of natural language inputs and generating appropriate responses.

Natural language generation: Creating natural language outputs that are coherent, fluent, and relevant to the context.Reference::What is Natural Language Processing? | IBM,Natural language processing - Wikipedia

Generative Adversarial Networks (GANs) are a type of AI model that can generate realistic images that resemble training data. The architecture of a GAN consists of two separate neural networks that are pitted against each other in a game-like scenario. The first network, known as the generator network, tries to create fake data that looks real. The second network, known as the discriminator network, tries to distinguish between real and fake data. The generator network learns from the feedback of the discriminator network and tries to fool it by improving the quality of the fake data. The discriminator network also learns from the feedback of the generator network and tries to improve its accuracy.The process continues until the generator network produces data that is indistinguishable from the real data4.GANs can be used to create realistic images of faces, animals, landscapes, and more5.Reference:Generative models - OpenAI,Artificial Intelligence Explained: What Are Generative Adversarial ...

Computer Vision is an AI domain that is associated with tasks such as recognizing faces in images and classifying objects. Computer vision is a field of artificial intelligence that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs, and to take actions or make recommendations based on that information. Computer vision works by applying machine learning and deep learning models to visual data, such as pixels, colors, shapes, textures, etc., and extracting features and patterns that can be used for various purposes. Some of the common techniques and applications of computer vision are:

Face recognition: Identifying or verifying the identity of a person based on their facial features.

Object detection: Locating and labeling objects of interest in an image or a video.

Object recognition: Classifying objects into predefined categories, such as animals, vehicles, fruits, etc.

Scene understanding: Analyzing the context and semantics of a visual scene, such as the location, time, weather, activity, etc.

Image segmentation: Partitioning an image into multiple regions that share similar characteristics, such as color, texture, shape, etc.

Image enhancement: Improving the quality or appearance of an image by applying filters, transformations, or corrections.

Image generation: Creating realistic or stylized images from scratch or based on some input data, such as sketches, captions, or attributes.Reference::What is Computer Vision? | IBM,Computer vision - Wikipedia

Oracle Cloud Infrastructure Speech is an AI service that applies automatic speech recognition (ASR) technology to transform audio-based content into text. Developers can easily make API calls to integrate Speech's pretrained models into their applications. Speech can be used for accurate, text-normalized, time-stamped transcription via the console and REST APIs as well as command-line interfaces or SDKs. You can also use Speech in an OCI Data Science notebook session.With Speech, you can filter profanities, get confidence scores for both single words and complete transcriptions, and more1.Reference:Speech AI Service that Uses ASR | OCI Speech - Oracle