|
Keyword Spotting
Keyword spotting (or more simply, word spotting) is a problem that was historically first defined in the context of speech processing. In speech processing, keyword spotting deals with the identification of keywords in utterances. Keyword spotting is also defined as a separate, but related, problem in the context of document image processing. In document image processing, keyword spotting is the problem of finding all instances of a query word that exist in a scanned document image, without fully recognizing it. In speech processing The first works in keyword spotting appeared in the late 1980s. A special case of keyword spotting is wake word (also called hot word) detection used by personal digital assistants such as Alexa or Siri to activate the dormant speaker, in other words "wake up" when their name is spoken. In the United States, the National Security Agency has made use of keyword spotting since at least 2006. This technology allows analysts to search through large volu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Speech Processing
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. History Early attempts at speech processing and recognition were primarily focused on understanding a handful of simple phonetic elements such as vowels. In 1952, three researchers at Bell Labs, Stephen. Balashek, R. Biddulph, and K. H. Davis, developed a system that could recognize digits spoken by a single speaker. Pioneering works in field of speech recognition using analysis of its spectrum were reported in the 1940s. Linear predictive coding (LPC), a sp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Keyword (linguistics)
In corpus linguistics a key word is a word which occurs in a text more often than we would expect to occur by chance alone. Key words are calculated by carrying out a statistical test (e.g., loglinear or chi-squared) which compares the word frequencies in a text against their expected frequencies derived in a much larger corpus, which acts as a reference for general language use. Keyness is then the quality a word or phrase has of being "key" in its context. Combinations of nouns with parts of speech that human readers would not likely notice, such as prepositions, time adverbs, and pronouns can be a relevant part of keyness. Even separate pronouns can constitute keywords. Compare this with collocation, the quality linking two words or phrases usually assumed to be within a given span of each other. Keyness is a ''textual'' feature, not a language feature (so a word has keyness in a certain textual context but may well not have keyness in other contexts, whereas a node and colloca ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Utterance
In spoken language analysis, an utterance is a continuous piece of speech, by one person, before or after which there is silence on the part of the person. In the case of oral language, spoken languages, it is generally, but not always, bounded by silence. In written language, utterances only exist indirectly, though their representations or portrayals. They can be represented and delineated in written language in many ways. In spoken language, utterances have several characteristics such as paralinguistic features, which are aspects of speech such as facial expression, gesture, and posture. Prosody (linguistics) , Prosodic features include Stress (linguistics), stress, Intonation (linguistics), intonation, and Paralanguage, tone of voice, as well as ellipsis, which are words that the listener inserts in spoken language to fill gaps. Moreover, other aspects of utterances found in spoken languages are non-fluency features including: voiced/un-voiced pauses (i.e. "umm"), tag quest ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Amazon Alexa
Amazon Alexa is a virtual assistant technology marketed by Amazon and implemented in software applications for smart phones, tablets, wireless smart speakers, and other electronic appliances. Alexa was largely developed from a Polish speech synthesizer named Ivona, acquired by Amazon in January 24, 2013. Alexa was first used in the Amazon Echo smart speaker and the Amazon Echo Dot, Echo Studio and Amazon Tap speakers developed by Amazon Lab126. It is capable of natural language processing for tasks such as voice interaction, music playback, creating to-do lists, setting alarms, streaming podcasts, playing audiobooks, providing weather, traffic, sports, other real-time information and news. Alexa can also control several smart devices as a home automation system. Alexa's capabilities may be extended by installing "skills" (additional functionality developed by third-party vendors, in other settings more commonly called apps) such as weather programs and audio feature ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Apple Siri
Siri ( , backronym: Speech Interpretation and Recognition Interface) is a digital assistant purchased, developed, and popularized by Apple Inc., which is included in the iOS, iPadOS, watchOS, macOS, Apple TV, audioOS, and visionOS operating systems. It uses voice queries, gesture based control, focus-tracking and a natural-language user interface to answer questions, make recommendations, and perform actions by delegating requests to a set of Internet services. With continued use, it adapts to users' individual language usages, searches, and preferences, returning individualized results. Siri is a spin-off from a project developed by the SRI International Artificial Intelligence Center. Its speech recognition engine was provided by Nuance Communications, and it uses advanced machine learning technologies to function. Its original American, British, and Australian voice actors recorded their respective voices around 2005, unaware of the recordings' eventual usage. Siri was re ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
National Security Agency
The National Security Agency (NSA) is an intelligence agency of the United States Department of Defense, under the authority of the director of national intelligence (DNI). The NSA is responsible for global monitoring, collection, and processing of information and data for global intelligence and counterintelligence purposes, specializing in a discipline known as signals intelligence (SIGINT). The NSA is also tasked with the protection of U.S. communications networks and information systems. The NSA relies on a variety of measures to accomplish its mission, the majority of which are clandestine. The NSA has roughly 32,000 employees. Originating as a unit to decipher coded communications in World War II, it was officially formed as the NSA by President Harry S. Truman in 1952. Between then and the end of the Cold War, it became the largest of the U.S. intelligence organizations in terms of personnel and budget. Still, information available as of 2013 indicates that the C ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
IARPA
The Intelligence Advanced Research Projects Activity (IARPA) is an organization, within the Office of the Director of National Intelligence (ODNI), that is responsible for leading research to overcome difficult challenges facing the United States Intelligence Community. IARPA characterizes its mission as follows: "To envision and lead high-risk, high-payoff research that delivers innovative technology for future overwhelming intelligence advantage." IARPA funds academic and industry research across a broad range of technical areas, including mathematics, computer science, physics, chemistry, biology, neuroscience, linguistics, political science, and cognitive psychology. Most IARPA research is unclassified and openly published. IARPA transfers successful research results and technologies to other government agencies. Notable IARPA investments include quantum computing, superconducting computing, machine learning, and forecasting tournaments. Mission IARPA characterizes its m ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Babel Program
The IARPA Babel program developed speech recognition technology for noisy telephone conversations. The main goal of the program was to improve the performance of keyword search on languages with very little transcribed data, i.e. low-resource languages. Data from 26 languages was collected with certain languages being held-out as "surprise" languages to test the ability of the teams to rapidly build a system for a new language. Beginning in 2012, two industry-led teams (IBM and BBN) and two university-led teams ( ICSI led by Nelson Morgan and CMU) participated. The IBM team included University of Cambridge and RWTH Aachen University, while BBN's team included Brno University of Technology, Johns Hopkins University, MIT and LIMSI. Only BBN and IBM made it to the final evaluation campaign in 2016, in which BBN won by achieving the highest keyword search accuracy on the evaluation language. Some of the funding from Babel was used to further develop the Kaldi toolkit. The speech dat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sliding Window
A sliding window protocol is a feature of packet-based data transmission Protocol (computing), protocols. Sliding window protocols are used where reliable in-order delivery of packets is required, such as in the data link layer (OSI model#Layer 2: Data link layer, OSI layer 2) as well as in the Transmission Control Protocol (i.e., TCP windowing). They are also used to improve efficiency when the channel may include high Network delay, latency. Packet-based systems are based on the idea of sending a batch of data, the ''packet'', along with additional data that allows the receiver to ensure it was received correctly, perhaps a checksum. The paradigm is similar to a window sliding sideways to allow entry of fresh packets and reject the ones that have already been acknowledged. When the receiver verifies the data, it sends an Acknowledgement (data networks), acknowledgment signal, or ACK, back to the sender to indicate it can send the next packet. In a simple automatic repeat requ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Garbage Model
Garbage, trash (American English), rubbish (British English), or refuse is waste material that is discarded by humans, usually due to a perceived lack of utility. The term generally does not encompass bodily waste products, purely liquid or gaseous wastes, or toxic waste products. Garbage is commonly sorted and classified into kinds of material suitable for specific kinds of disposal. Terminology The word ''garbage'' originally meant chicken giblets and other entrails, as can be seen in the 15th century Boke of Kokery, which has a recipe for ''Garbage''. What constitutes garbage is highly subjective, with some individuals or societies tending to discard things that others find useful or restorable. The words garbage, refuse, rubbish, trash, and waste are generally treated as interchangeable when used to describe "substances or objects which the holder discards or intends or is required to discard". Some of these terms have historic distinctions that are no longer present. In ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Iterative Viterbi Decoding
Iterative Viterbi decoding is an algorithm that spots the subsequence ''S'' of an observation ''O'' = having the highest average probability (i.e., probability scaled by the length of ''S'') of being generated by a given hidden Markov model ''M'' with ''m'' states. The algorithm uses a modified Viterbi algorithm as an internal step. The scaled probability measure was first proposed by John S. Bridle. An early algorithm to solve this problem, sliding window, was proposed by Jay G. Wilpon et al., 1989, with constant cost ''T'' = ''mn''2/2. A faster algorithm consists of an iteration of calls to the Viterbi algorithm, reestimating a filler score until convergence. The algorithm A basic (non-optimized) version, finding the sequence ''s'' with the smallest normalized distance from some subsequence of ''t'' is: // input is placed in observation s[1..n], template t[1..m], // and distance matrix d[1..n,1..m] // remaining elements in matrices are solely for internal computations (int ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
