Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech computer or 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.
A speech disfluency, also spelled speech dysfluency, is any of various breaks, irregularities, or non-lexical vocables that occurs within the flow of otherwise fluent speech. These include false starts, i.e. words and sentences that are cut off mid-utterance; phrases that are restarted or repeated and repeated syllables; fillers, i.e. grunts or non-lexical utterances such as "huh", "uh", "erm", "um", "well", "so", "like", and "hmm"; and repaired utterances, i.e. instances of speakers correcting their own slips of the tongue or mispronunciations. "Huh" is claimed to be a universal syllable.
In corpus linguistics, part-of-speech tagging, also called grammatical tagging or word-category disambiguation, is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context—i.e., its relationship with adjacent and related words in a phrase, sentence, or paragraph. A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc.
The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language. It was developed by Steven Bird and Edward Loper in the Department of Computer and Information Science at the University of Pennsylvania. NLTK includes graphical demonstrations and sample data. It is accompanied by a book that explains the underlying concepts behind the language processing tasks supported by the toolkit, plus a cookbook.
A statistical language model is a probability distribution over sequences of words. Given such a sequence, say of length m, it assigns a probability to the whole sequence.
CMU Sphinx, also called Sphinx in short, is the general term to describe a group of speech recognition systems developed at Carnegie Mellon University. These include a series of speech recognizers and an acoustic model trainer (SphinxTrain).
Julius is a speech recognition engine, specifically a high-performance, two-pass large vocabulary continuous speech recognition (LVCSR) decoder software for speech-related researchers and developers. It can perform almost real-time computing (RTC) decoding on most current personal computers (PCs) in 60k word dictation task using word trigram (3-gram) and context-dependent Hidden Markov model (HMM). Major search methods are fully incorporated. It is also modularized carefully to be independent from model structures, and various HMM types are supported such as shared-state triphones and tied-mixture models, with any number of mixtures, states, or phones. Standard formats are adopted to cope with other free modeling toolkit. The main platform is Linux and other Unix workstations, and it works on Windows. Julius is free and open-source software, released under a revised BSD style software license.
VoxForge is a free speech corpus and acoustic model repository for open source speech recognition engines.
The CSLU Toolkit is a software library comprising a comprehensive suite of tools that enable exploration, learning, and research into speech and human-computer interaction. It is developed by the Center for Spoken Language Understanding at the OGI School of Science and Engineering, a school of the Oregon Health & Science University.
An acoustic model is used in automatic speech recognition to represent the relationship between an audio signal and the phonemes or other linguistic units that make up speech. The model is learned from a set of audio recordings and their corresponding transcripts. It is created by taking audio recordings of speech, and their text transcriptions, and using software to create statistical representations of the sounds that make up each word.
As of the early 2000s, several speech recognition (SR) software packages exist for Linux. Some of them are free and open-source software and others are proprietary software. Speech recognition usually refers to software that attempts to distinguish thousands of words in a human language. Voice control may refer to software used for communicating operational commands to a computer.
Tk is a free and open-source, cross-platform widget toolkit that provides a library of basic elements of GUI widgets for building a graphical user interface (GUI) in many programming languages.
RWTH ASR is a proprietary speech recognition toolkit.
Professor Nelson Morgan is the former director of the International Computer Science Institute (ICSI), where he was also the Speech Group leader. He is also a professor in residence (emeritus) of electrical engineering and computer science at the University of California, Berkeley. He recently co-founded UpRise Campaigns, a California Social Purpose Corporation focused on campaign reform through empowering volunteerism.
Janus Recognition Toolkit (JRTk), sometimes referred to as Janus, is a general purpose speech recognition toolkit developed and maintained by the Interactive Systems Laboratories at Carnegie Mellon University and Karlsruhe Institute of Technology. It is useful for both research and application development and is part of the JANUS speech-to-speech translation system.
Stephen John Young FREng is a British researcher, Professor of Information Engineering at the University of Cambridge and an entrepreneur. He is one of the pioneers of automated speech recognition and statistical spoken dialogue systems. He served as the Senior Pro-Vice-Chancellor of the University of Cambridge from 2009 to 2015, responsible for Planning and Resources. He currently holds a joint appointment between his professorship at Cambridge and Apple, where he is a senior member of the Siri development team.
openSMILE is an open-source software for automatic extraction of features from audio signals and for classification of speech and music signals. "SMILE" stands for "Speech & Music Interpretation by Large-space Extraction". The software is mainly applied in the area of automatic emotion recognition and is widely used in the affective computing research community. The openSMILE project exists since 2008 and is maintained by the German company audEERING GmbH since 2013. openSMILE is provided free of charge for research purposes and personal use under an open-source license. For commercial use of the tool, the company audEERING offers custom license options.
