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Speech synthesis is when computers talk like people. It helps us talk to machines and listen to them, just like we do with our friends.

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Phonetics is the branch of linguistics that studies the physical sounds of human speech, focusing on their production, acoustic properties, and auditory perception. It provides the foundational understanding necessary for analyzing how sounds are articulated and distinguished in different languages.
Natural language processing (NLP) is a field at the intersection of computer science, artificial intelligence, and linguistics, focused on enabling computers to understand, interpret, and generate human language. It encompasses a wide range of applications, from speech recognition and sentiment analysis to machine translation and conversational agents, leveraging techniques like machine learning and deep learning to improve accuracy and efficiency.
Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience. It leverages data to train models that can make predictions or decisions without being explicitly programmed for specific tasks.
Artificial intelligence refers to the development of computer systems capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. It encompasses a range of technologies and methodologies, including machine learning, neural networks, and natural language processing, to create systems that can learn, adapt, and improve over time.
Concept
Prosody refers to the rhythm, stress, and intonation of speech, playing a crucial role in conveying meaning, emotion, and intention beyond the literal words spoken. It is essential in both spoken language comprehension and effective communication, influencing how messages are interpreted and understood by listeners.
Speech Signal Processing involves the analysis and manipulation of speech signals to enhance, recognize, or synthesize human speech. It plays a critical role in various applications such as voice recognition systems, hearing aids, and telecommunications, leveraging advanced algorithms to improve clarity and intelligibility of speech in diverse environments.
Speech-Generating Devices (SGDs) are electronic devices that produce spoken language output, aiding individuals with speech impairments in communication. They are crucial tools in augmentative and alternative communication (AAC), providing users with a voice and enhancing their ability to interact with others effectively.
Text-to-Speech Synthesis (TTS) is a technology that converts written text into spoken words, enabling computers to 'speak' by using artificial voices. It combines natural language processing to understand and process text with digital signal processing to generate human-like speech, providing accessibility and convenience in various applications such as virtual assistants and audiobooks.
Speech-to-text technology, also known as automatic speech recognition (ASR), converts spoken language into written text through the use of algorithms and machine learning models. This technology is widely used in applications such as virtual assistants, transcription services, and accessibility tools, enhancing user interaction and accessibility in digital environments.
Machine learning in speech processing leverages algorithms to automatically recognize and interpret human speech, enabling applications like voice recognition, transcription, and language translation. It involves training models on large datasets to improve accuracy and adaptability to different accents and languages.
Vocal tract acoustics explores how the shape and movement of the vocal tract influence sound production, focusing on the modulation of air flow and resonance to produce speech and singing. It bridges the gap between physical vocal tract configurations and the acoustic properties of the sounds produced, essential for understanding speech production and voice synthesis.
Speech dynamics refers to the temporal and spectral variations in speech sounds, encompassing how speech changes over time and across different frequencies. It is crucial for understanding speech production, perception, and the mechanisms underlying speech disorders.
Formant frequencies are the resonant frequencies of the vocal tract that shape the sound of speech, making them crucial for distinguishing between different vowels. They are determined by the shape and size of the vocal tract and are key in speech analysis and synthesis.
Concept
Formants are resonant frequencies of the vocal tract that significantly influence the timbre and phonetic quality of speech sounds. They are crucial for distinguishing between different vowels and are used in speech analysis and synthesis to understand and replicate human speech.
Automated Speech Recognition (ASR) is a technology that enables the conversion of spoken language into text by computers, facilitating hands-free operation and accessibility. It leverages complex algorithms and machine learning to understand and process human speech, making it a cornerstone in applications ranging from virtual assistants to transcription services.
Vocal formants are resonant frequencies in the vocal tract that shape the unique qualities of our speech sounds, playing a crucial role in distinguishing different vowels and consonants. They are determined by the shape and configuration of the vocal tract and are essential for the clarity and individuality of a person's voice.
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Siri is a virtual assistant developed by Apple Inc., designed to facilitate user interaction with Apple devices through voice recognition and natural language processing. It leverages machine learning algorithms to understand and respond to user queries, providing a seamless and intuitive user experience across various Apple platforms.
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