Speech processing is the study of speech signals and the processing methods of signals. The signals are usually processed in a digital representation, so speech processing can be regarded as a special case of digital signal processing, applied to speech signals. Aspects of speech processing includes the acquisition, manipulation, storage, transfer and output of speech signals. Different speech processing tasks include speech recognition, speech synthesis, speaker diarization, speech enhancement, speaker recognition, etc.
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis.
Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech. The reverse process is speech recognition.
Lip reading, also known as speechreading, is a technique of understanding a limited range of speech by visually interpreting the movements of the lips, face and tongue without sound. Estimates of the range of lip reading vary, with some figures as low as 30% because lip reading relies on context, language knowledge, and any residual hearing. Although lip reading is used most extensively by deaf and hard-of-hearing people, most people with normal hearing process some speech information from sight of the moving mouth.
Visible Speech is a system of phonetic symbols developed by British linguist Alexander Melville Bell in 1867 to represent the position of the speech organs in articulating sounds. Bell was known internationally as a teacher of speech and proper elocution and an author of books on the subject. The system is composed of symbols that show the position and movement of the throat, tongue, and lips as they produce the sounds of language, and it is a type of phonetic notation. The system was used to aid the deaf in learning to speak.
A viseme is any of several speech sounds that look the same, for example when lip reading.
Human image synthesis is technology that can be applied to make believable and even photorealistic renditions of human-likenesses, moving or still. It has effectively existed since the early 2000s. Many films using computer generated imagery have featured synthetic images of human-like characters digitally composited onto the real or other simulated film material. Towards the end of the 2010s deep learning artificial intelligence has been applied to synthesize images and video that look like humans, without need for human assistance, once the training phase has been completed, whereas the old school 7D-route required massive amounts of human work .
Meta-learning is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing learning algorithms or to learn (induce) the learning algorithm itself, hence the alternative term learning to learn.
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Bayesian optimization is a sequential design strategy for global optimization of black-box functions, that does not assume any functional forms. It is usually employed to optimize expensive-to-evaluate functions. With the rise of artificial intelligence innovation in the 21st century, Bayesian optimizations have found prominent use in machine learning problems, for optimizing hyperparameter values.
Google Brain was a deep learning artificial intelligence research team that served as the sole AI branch of Google before being incorporated under the newer umbrella of Google AI, a research division at Google dedicated to artificial intelligence. Formed in 2011, it combined open-ended machine learning research with information systems and large-scale computing resources. It created tools such as TensorFlow, which allow neural networks to be used by the public, and multiple internal AI research projects, and aimed to create research opportunities in machine learning and natural language processing. It was merged into former Google sister company DeepMind to form Google DeepMind in April 2023.
Emotion recognition is the process of identifying human emotion. People vary widely in their accuracy at recognizing the emotions of others. Use of technology to help people with emotion recognition is a relatively nascent research area. Generally, the technology works best if it uses multiple modalities in context. To date, the most work has been conducted on automating the recognition of facial expressions from video, spoken expressions from audio, written expressions from text, and physiology as measured by wearables.
An AI accelerator, deep learning processor or neural processing unit (NPU) is a class of specialized hardware accelerator or computer system designed to accelerate artificial intelligence and machine learning applications, including artificial neural networks and computer vision. Typical applications include algorithms for robotics, Internet of Things, and other data-intensive or sensor-driven tasks. They are often manycore designs and generally focus on low-precision arithmetic, novel dataflow architectures or in-memory computing capability. As of 2024, a typical AI integrated circuit chip contains tens of billions of MOSFETs.
AlexNet is a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton, who was Krizhevsky's Ph.D. advisor at the University of Toronto in 2012. It had 60 million parameters and 650,000 neurons.
WaveNet is a deep neural network for generating raw audio. It was created by researchers at London-based AI firm DeepMind. The technique, outlined in a paper in September 2016, is able to generate relatively realistic-sounding human-like voices by directly modelling waveforms using a neural network method trained with recordings of real speech. Tests with US English and Mandarin reportedly showed that the system outperforms Google's best existing text-to-speech (TTS) systems, although as of 2016 its text-to-speech synthesis still was less convincing than actual human speech. WaveNet's ability to generate raw waveforms means that it can model any kind of audio, including music.
Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. Their creation was inspired by biological neural circuitry. While some of the computational implementations ANNs relate to earlier discoveries in mathematics, the first implementation of ANNs was by psychologist Frank Rosenblatt, who developed the perceptron. Little research was conducted on ANNs in the 1970s and 1980s, with the AAAI calling this period an "AI winter".
15.ai was a free to use artificial intelligence web application that generated text-to-speech voices from fictional characters from various media sources. Created by a pseudonymous developer under the alias 15, the project used a combination of audio synthesis algorithms, speech synthesis deep neural networks, and sentiment analysis models to generate emotive character voices.
Alex Krizhevsky is a Ukrainian-born Canadian computer scientist most noted for his work on artificial neural networks and deep learning. In 2012, Krizhevsky, Ilya Sutskever and their PhD advisor Geoffrey Hinton, at the University of Toronto, developed a powerful visual-recognition network AlexNet using only two GeForce NVIDIA GPU cards. This revolutionized research in neural networks. Previously neural networks were trained on CPUs. The transition to GPUs opened the way to the development of advanced AI models. AlexNet won the ImageNet challenge in 2012. Krizhevsky and Sutskever sold their startup, DNN Research Inc., to Google, shortly after winning the contest. Krizhevsky left Google in September 2017 after losing interest in the work, to work at the company Dessa in support of new deep-learning techniques. Many of his numerous papers on machine learning and computer vision are frequently cited by other researchers. He is also the main author of the CIFAR-10 and CIFAR-100 datasets.
Audio deepfake technology, also referred to as voice cloning or deepfake audio, is an application of artificial intelligence designed to generate speech that convincingly mimics specific individuals, often synthesizing phrases or sentences they have never spoken. Initially developed with the intent to enhance various aspects of human life, it has practical applications such as generating audiobooks and assisting individuals who have lost their voices due to medical conditions. Additionally, it has commercial uses, including the creation of personalized digital assistants, natural-sounding text-to-speech systems, and advanced speech translation services.
Deep learning speech synthesis refers to the application of deep learning models to generate natural-sounding human speech from written text (text-to-speech) or spectrum (vocoder). Deep neural networks are trained using large amounts of recorded speech and, in the case of a text-to-speech system, the associated labels and/or input text.
