Voice synthesizer application android

Continue The Download Now link sends you to the Windows Store, where you can continue the download process. You need to have an active Microsoft account to download the app. This download may not be available in some countries. Results 1 - 10 of 603 Prev 1 2 3 4 5 Next See also: Receming Device is an artificial production of human speech. The computer system used for this purpose is called a speech computer or speech synthesizer, and can be implemented in software or hardware. The text-to-speech system (TTS) converts the usual text of into speech; other systems display symbolic linguistic representations, such as phonetic transcriptions in speech. Synthesized speech can be created by concatenating fragments of recorded speech that are stored in the database. Systems vary in size of stored speech blocks; The system that stores phones or diphones provides the greatest range of outputs, but may not have clarity. For specific domain use, storing whole words or suggestions allows for high-quality output. In addition, the synthesizer may include a vocal tract model and other characteristics of the human voice to create a fully synthetic voice output. The quality of the speech synthesizer is judged by its similarity to the human voice and its ability to be understood clearly. The clear text to speech program allows people with visual impairments or reading disabilities to listen to written words on their home computer. Many computer operating systems have included speech synthesizers since the early 1990s. A review of the typical TTS Automatic Announcement System synthetic voice announces the arriving train to Sweden. Problems with playing this file? See the media report. The text to speech system (or engine) consists of two parts: front and back. The front end has two main tasks. First, it converts raw text containing symbols, such as numbers and abbreviations, into the equivalent of written words. This process is often referred to as text normalization, pre-processing, or tokenization. The front end then assigns phonetic transcriptions to each word, and divides and marks the text into prozodiotic units such as phrases, clauses and sentences. The process of assigning phonetic transcriptions to words is called the transformation of text into a phoneme or graphema to a phoneme. Phonetic transcriptions and prosody information together constitute a symbolic linguistic representation that is the output at the front end. The rear end, often called a synthesizer, converts a symbolic linguistic representation into sound. In some systems, this part includes the calculation of the target prosody (step outline, phoneme duration), which is then imposed on output speech. Long before the invention of electronic signal processing, some people tried to build machines to emulate human speech. Speech. Early legends of the existence of Brazen Heads involved Pope Sylvester II (d. 1003 AD), Albert Magnus (1198-1280), and Roger Bacon (1214-1294). In 1779, the German-Danish scientist Christian Gottlieb Kratzenstein received the first prize in a competition announced by the Russian Imperial Academy of and Arts for models he built from a human vocal tract that could produce five long vowels (in the international phonetic notation of the alphabet: aː, eː, iː, oː and uː). It was followed by the acoustic-mechanical speech machine of of Pressburg, Hungary, described in a 1791 article. This machine added models of tongue and lips, which allowed it to produce consonants as well as vowels. In 1837, Charles Wheatstone produced a talking machine designed by von Kempelen, and in 1846, Joseph Faber exhibited Euphonia. In 1923, Peydt revived Wheatstone's design. In the 1930s, developed a vocoder that automatically analyzed speech in its fundamental tones and resonances. From his work on the vocoder, Homer Dudley developed a voice synthesizer called The Voder (Voice Demonstrator), which he exhibited at the 1939 World's Fair in New York. Dr. Franklin S. Cooper and his colleagues at Haskins Lab built a pattern reproduction in the late 1940s and completed it in 1950. There were several different versions of this hardware device; Only one currently survives. The machine converts images of acoustic speech patterns in the form of a spectrogram back into sound. Using this device, Alvin Lieberman and his colleagues found acoustic signals for the perception of phonetic segments (consonants and vowels). Electronic computer and speech synthesizer devices used by Stephen Hawking in 1999 The first computer speech synthesis systems originated in the late 1950s. Noriko Umeda et al. developed the first common English text-speech system in 1968, at the Electrical Laboratory in Japan. In 1961, physicist John Larry Kelly Jr. and his colleague Louis Gerstman used an IBM 704 computer to synthesize speech, one of the most famous events in bell Labs history. (quote necessary) Kelly's synthesizer recorder (vocoder) recreated the song Daisy Bell to the music of Max Matthews. Coincidentally, Arthur Clarke was visiting his friend and colleague John Pierce at the Bell Labs Murray Hill facility. Clarke was so impressed by the demonstration that he used it in the climax scene of his script for his 2001 novel: A Space Odyssey, 10 where a HAL 9000 computer sings the same song as astronaut Dave Bowman puts him to sleep. Despite the success of purely electronic speech synthesis, into mechanical speech synthesizers continues. Linear predictive coding (required third-party source) The form of speech coding began with the work of Fumitada Ithaku from the University of Nagoya and Shuzo Saito of Nippon Telegraph and Telephone (NTT) in 1966. Further developments in LPC were made by Bishna S. Atal and Manfred R. Schroeder at Bell Labs in the 1970s. In 1975, Fumitada Ithaku developed the Linear Spectral Pairs (LSP) method for coding high-compression speech while in NTT. From 1975 to 1981, Ithaca studied problems in speech analysis and synthesis based on the LSP method. In 1980, his team developed a speech synthesizer chip based on LSP. LSP is an important technology for speech synthesis and coding, and in the 1990s was adopted by almost all international standards of speech coding as an important component, helping to improve digital speech through mobile channels and the Internet. In 1975, MUSA was released and became one of the first speech synthesis systems. It consisted of independent computer equipment and specialized software that allowed him to read Italian. The second version, released in 1978, was also able to advise in Italian in the style of a cappella. The DECtalk demo, using perfect Paul and Uppity Ursula voices Dominant in the 1980s and 1990s, was a DECtalk system based primarily on the work of Dennis Klatt at MIT and Bell Labs; The latter was one of the first multilingual language independent systems, using natural language processing techniques extensively. Hand electronics with speech synthesis began to appear in the 1970s. One of the first was the portable Telesensory Systems Inc. (TSI) Speech calculator for the blind in 1976. Other devices had mostly educational purposes, such as the Speak toy and the Spell produced by Texas Instruments in 1978. In 1979, Fidelity released a colloquial version of its electronic chess computer. The first video game to feature speech synthesis was the 1980 arcade game Stratovox (known in Japan as Speak and Rescue) by Sun Electronics. The first personalized speech-synthesis computer game was Manbiki Shoujo (Shoplifting Girl), released in 1980 for PET 2001, for which the game's developer, Hiroshi Suzuki, developed a zero cross programming method for the production of synthesized speech form. Another early example, the arcade version of Berzerk, also dates back to 1980. Milton Bradley released the first multiplayer electronic game using Milton's voice synthesis in the same year. Early electronic speech synthesizers sounded robotic and were often barely clear. The quality of synthesized speech is steadily improving, but compared to 2016, the output from modern speech synthesis remains clearly distinguishable from actual human speech. Synthesized voices tended to sound male until 1990, when Anne Sirdal, of ATT Bell Laboratories, created a female voice. In 2005, Kurzweil predicted that as the cost-performance ratio made speech synthesizers cheaper and more accessible, more people would benefit from text programs. Synthesizer technology The most important qualities of the speech synthesis system are naturalness and intelligibility. Naturalness describes how close the output sounds like human speech, while intelligibility is the ease with which output is understood. The ideal speech synthesizer is natural and understandable. Speech synthesis systems usually try to maximize both characteristics. The two main for generating synthetic speech waves are and formal synthesis. Each technology has strengths and weaknesses, and the intended use of a fusion system usually determines which approach is used. Synthesis of Concatenation Main article: Concatenative synthesis is based on concatenation (or stringing together) segments of recorded speech. Typically, concatinative synthesis produces the most naturally sounding synthesized speech. However, differences between natural variations of speech and the nature of automated wave segmentation methods sometimes lead to sound failures in output. There are three main subtypes of concatinative synthesis. The synthesis of the group's choice unit synthesis uses large databases of recorded speech. While creating the database, each recorded statement is segmented into some or all of the following: individual phones, diphons, halfphones, syllables, morphemes, words, phrases, and sentences. Typically, segmentage is divided using specially modified speech recognition, set up in forced alignment mode with some manual correction after that, using visual representations such as waveform and spectrogram. The unit index is then created in a speech database based on segmentation and acoustic parameters, such as fundamental frequency (step), duration, syllable position, and adjacent phones. During time time, the desired target statement is created by determining the best chain of units of candidates from the database (unit selection). This process is usually achieved with a specially weighted solution tree. Selecting a unit provides the greatest naturalness because it only applies a small amount of digital signal processing (DSP) to recorded speech. DSP often makes recorded speech sound less natural, although some systems use a small amount of signal processing at the point of concatenation to smooth out the shape of the wave. Output the best unit selection systems are often indistinguishable from real human especially in the environment for which the TTC system has been configured. However, maximum naturalness usually requires a unit choosing speech databases to be very large, in some systems starting in gigabytes of recorded data representing tens of hours of speech. In addition, it is known that the algorithms of selecting units select segments from a place that leads to less ideal synthesis (for example, minor words become unclear), even if there is a better choice in the database. Recently, researchers have proposed various automated methods for detecting unnatural segments in speech synthesis systems when selecting units. Diphone synthesis Diphone synthesis uses a minimal speech database containing all diphons (sound transitions) occurring in the language. The number of diphons depends on the phonothics of the language: for example, Spanish has about 800 diphones, and German about 2500. The synthesis of diphons in the speech database contains only one example of each diphon. During execution, the sentence target is superimposed on these minimum units using digital signal processing methods, such as linear predictive coding, PSOLA, or MBROLA. or later methods, such as a step modification in the source domain using a discrete cosine conversion. Diphon synthesis suffers from sound failures of concatentative synthesis and the robotic nature of formant synthesis, and has few advantages of either approach except a small size. Thus, its use in commercial applications is declining, although it is still used in research because there are a number of freely available software implementations. An early example of Diphon's synthesis is a training robot leaching that was invented by Michael Freeman. Lichim provided information about the curriculum and some biographical information about the 40 students he was programmed to teach. It was tested in fourth grade in the Bronx, New York. Domain-specific domain synthesis concats pre-recorded words and phrases to create complete utterances. It's used in applications where the variety of texts that the system will display is limited to certain domains, such as transit schedule announcements or weather reports. The technology is very easy to implement, and has been in commercial use for a long time, in devices such as talking clocks and calculators. The level of naturalness of these systems can be very high, because the variety of types of sentences is limited, and they closely correspond to the prosody and intonation of the original recordings. Because these systems are limited by words and phrases in their databases, they are not common goals and can only synthesize combinations of words and phrases with which they have been programmed. within naturally spoken language however can still cause problems if many changes are not taken into account. For example, in non-original English dialects, the word r in words as clear /ˈklɪə/ is usually pronounced only when the following word has a vowel as the first letter (for example, purr is implemented as /ˌklɪəɹˈʌʊt/). Similarly in French, many final consonants become no longer silent if you follow a word that begins with a vowel, an effect called communication. This alternation cannot be replicated by a simple word concation system that will require additional complexity to be context sensitive. Formant synthesis formant synthesis does not use samples of human speech during execution. Instead, synthesized speech output is created using and acoustic model (synthesis of physical modeling). Options, such as fundamental frequency, voice and noise level, change over time, creating a wave form of artificial speech. This method is sometimes called rule-based synthesis; however, many concatathive systems also have rule-based components. Many systems based on formant synthesis technology generate artificial, robotic speech that will never be mistaken for human speech. However, maximum naturalness is not always the goal of the speech synthesis system, and formant synthesis systems have advantages over concatenative systems. Formal-synthesized speech can be reliably comprehensible, even at very high speeds, avoiding the acoustic failures that usually plague concataent systems. High-speed synthesized speech is used by the visually impaired to quickly navigate computers with a . Formant synthesizers tend to have fewer programs than concataent systems because they do not have a database of speech samples. Therefore, they can be used in built-in systems where memory and microprocessor power are particularly limited. Since formant-based systems have complete control over all aspects of speech output, a wide range of prosed and intonations can be an outlet, conveying not only questions and statements, but different and tone of voice. Examples not in real time, but very precise control of intonation in formant synthesis include work done in the late 1970s for Texas toy talking tools and spell, and early 1980s Sega arcade machines and in many Atari, Inc. arcade games using TMS5220 LPC Chips. Creating proper intonation for these projects has been painstaking, and the results have yet to be compared in real-time text to speech interfaces. The articulation synthesis of articulation synthesis refers to computational synthesis of speech based on models of the human voice tract and the articulation processes taking place there. The first articulation synthesizer, regularly used for laboratory experiments, was developed in Huskins' Labs in , Tom Baer and Paul Mermelstein in the mid-1970s. This synthesizer, known as ASY, was based on models of vocal treatises developed at Bell Laboratories in the 1960s and 1970s by Paul Mehrmelstein, Cecil Cocker and his colleagues. Until recently, articulation fusion models were not included in commercial speech synthesis systems. A notable exception is the NeXT-based system originally developed and aligned by Trillium Sound Research, a spin-off of the University of Calgary, where most of the original research was conducted. After the demise of various NeXT incarnations (started by Steve Jobs in the late 1980s and merged with Apple Computer in 1997), Trillium software was published under the GNU General Public License, with the work continuing as a . The system, first introduced to the market in 1994, provides a complete conversion of text into speech based on articulation using the wave or transmission analogue of the oral and nasal pathways of humans controlled by the distinctive model of the region Carre. Later synthesizers developed by Jorge C. Lucero and his colleagues include models of voice fold , gultyal aerodynamics and acoustic wave distribution in the broncos, traque, nasal and oral cavities, and thus are complete speech modeling systems based on physics. HMM-based synthesis is a synthesis method based on Markov's hidden models, also called Statistical Parametric Synthesis. In this system, the frequency spectrum (vocal tract), the fundamental frequency (voice source) and the duration (proology) of speech are modeled simultaneously by HMMs. Speech wave forms are generated from the HMM themselves on the basis of the maximum probability criterion. Sinewave Sinewave synthesis is a method of speech synthesis that replaces formants (basic energy bands) with pure tonal-whistles. Deep synthesis-based training Formula Given input text or some sequence of the language block Y displaystyle Y, target speech X displaystyle X can be obtained X and arg ⁡ max P (X Y, θ) display Xarg max P(X Y, 'theta) where the θ 'display' theta is a model parameter. Typically, the input text is first transmitted to the acoustic function generator, then the acoustic functions are transferred to the neural vocoder. For the speaker generator, loss is usually the loss of L1 or L2. These loss functions limit the fact that the distributions of acoustic output functions should be Gaussian or Laplak. In practice, since a person's voice group ranges from 300 to 4000 Hz, the loss function will be designed to have more punishment on this range: l o s s s - α loss of a person ( 1 - α ) loss of other loss display alpha text text (text) is the loss of a person (mapping text) Human voice band and α displaystyle alpha is a scale, usually about 0.5. The acoustic feature is usually a spectrogram or spectrogram in the Chalk scale. These functions capture the time-frequency ratio of the speech signal and thus enough to generate intelligent outputs with these acoustic functions. The Mel-frequency cepstrum feature used in speech recognition is not suitable for speech synthesis because it reduces too much information. A Brief History In September 2016, DeepMind offered WaveNet, a deep generative model of untreated sound wave shapes. This shows the community that deep -based models are able to model untreated wave shapes and work well to generate speech from acoustic functions such as spectrograms or spectrograms on a chalk scale, or even from some pre-reworked linguistic features. In early 2017, Mila (a research institute) proposed char2wav, a model for producing a crude wave shape in the end-to-end method. In addition, Google and Facebook have offered Tacotron and VoiceLoop, respectively, to create acoustic features directly from the input text. Later that year, Google proposed Tacotron2, which combined WaveNet's vocoder with the revised Tacotron architecture to perform speech synthesis. Tacotron2 can generate quality speech that approaches the human voice. Since then, end methods have become the hottest topic of the study, because many researchers around the world are beginning to notice the power of end-end synth speech. The advantages and disadvantages of the practical methods are: you only need one model to perform text analysis, acoustic modeling and sound synthesis, i.e. the synthesis of speech directly from the symbols Less functions engineering Easily allows rich conditioning on various attributes, for example, the speaker or language Adaptation to new data is easier than multi-stage models, because the error of the component can aggravate the ability of the model to capture the hidden internal structures of the speech No need to support the large , i.e. a small footprint Despite the many benefits mentioned, end methods still have many problems to be solved: Auto-regressive models suffer from the slow problem of output Speech output are not reliable when the data are not sufficient lack of controllability compared to traditional concatateative and statistically parametric approaches Tendency to learn flat prosod by averaging over the training data, usually for the output of the because l1 or L2 loss is used by the withdrawal problem to solve the problem of slow withdrawal, Microsoft Research and Baidu research as suggested using non-auto-regressive models to make the withdrawal process faster. The FastSpeech model, proposed by Microsoft, uses the Transformer architecture with a duration model to achieve the goal. Goal. the duration model, borrowed from traditional methods, makes more reliable. - The problem of robustism Researchers have found that the problem of reliability is closely related to text alignment failures, and this causes many researchers to rethink the mechanism of attention that uses strong local relationships and monotonous properties of speech. - The problem of controllability To solve the problem of controllability is proposed a lot of work on a variation autoconcoder. (47) - The flat GST-Tacotron prosody problem can alleviate the problem of flat prosody a little bit, however, it still depends on the training data. - Smooth acoustic output problem To create more realistic acoustic functions, GAN learning strategies can be applied. In practice, however, a neural vocoder can generalize well, even if the input functions are smoother than the actual data. Semi-controlled learning Nowadays, self-managed learning gets a lot of attention because of the better use of unobtageable data. Studies show that the need for paired data decreases with self- control. The zero-shot speaker adaptation of the zero-shot speaker adaptation is promising because one model can generate speech with different speaker styles and characteristics. In June 2018, Google proposed using a pre-trained speaker check model as an speaker encoder to remove the speaker's embed. The speaker coder then becomes part of the neural text model to the speech, and it can determine the style and characteristic of the speech output. This shows the community that it is possible to use only one model to generate speech in multiple styles. Neural vocoder neural vocoder plays an important role in deep learning through speech synthesis to generate high-quality speech from acoustic features. The WaveNet model, introduced in 2016, achieves high performance in terms of speech quality. Wavenet considered the joint probability of waveform x x ∏ θ x_{1},...,x_ x 1, ...... He said ...... t 1) display style p_ x_ theta (matebaf x_{1},...,x_ (x) Where the display θ is a model setting that includes many extended layers of bundles. Thus, each sound sample x t displaystyle x_'t is therefore determined by samples at all times-steps. However, the automatically regressive nature of WaveNet makes the process sharply slow. Parallel WaveNet is offered to solve the problem of slow output, which comes from the auto-regressive characteristics of the WaveNet model. Parallel WaveNet is an auto- regulatory-based reverse model that learns to distill knowledge with a pre-trained WaveNet teacher model. Because the reverse model based on autoregressive flow is not auto-regressive when performing output, output, faster than real time. At the same time, Nvidia offered a WaveGlow-based flow model that can also generate speech faster than real-time speed. However, despite the high output rate, the WaveNet parallel has limited the need for a pre-trained WaveNet and WaveGlow model to take many weeks to get along with limited computing devices. This problem is solved by Parallel WaveGAN, which learns to produce speech using multi-volunteer spectral loss and GANs learning strategy. The problems text normalization problem Process the normalization of the text is rarely easy. The texts are full of heteronomes, numbers and abbreviations that all require expansion in phonetic representation. There are many spellings in English that are pronounced differently based on context. For example, my last project is to find out how best to draft my voice contains two pronunciations of the project. Most text-speech (TTS) systems do not generate semantic representations of their input texts because the processes for doing so are unreliable, poorly understood, and computationally inefficient. As a result, various heuristic techniques are used to guess the correct way to disambiguate homographers, like studying neighboring words and using statistics on the frequency of occurrence. Recently, TTS systems have started using HMMs (discussed above) to create parts of speech to help in disguise homographers. This method is highly successful in many cases, such as whether to read should be pronounced as red, implying the past tense, or as a cane implying the present. Typical error rates when using HMMs in this way tend to be below five percent. These methods also work well for most European , although access to the required teaching techniques is often difficult in these languages. Deciding how to convert numbers is another issue that TTS systems must address. It's a simple programming task to convert a number into words (at least in English) as 1325 becomes a thousand three hundred and twenty-five. However, numbers are found in many different contexts; 1325 can also be read as one three two five, thirteen twenty-five or thirteen hundred and twenty-five. The TTS system can often infer how to expand the number based on surrounding words, numbers, and punctuation, and sometimes the system provides a way to specify context if it is ambiguous. Roman numerals can also be read in different ways depending on the context. For example, Henry VIII is read as Henry the Eighth, while Chapter VIII is read as Chapter Eight. Similarly, the acronyms may be ambiguous. For example, the acronym in for inches should be differentiated from the word in, and the address of 12 St. John St. uses the same for Holy and Street. TTS systems with intelligent foregrounds can make educated guesses about ambiguous ambiguous while others provide the same result in all cases, resulting in meaningless (and sometimes comic) outlets such as Ulysses S. Grant turns out to be Ulysses South Grant. Text-to-phoneme challenges speech synthesis systems to determine the pronunciation of a word based on its spelling, a process that is often called text-to-phoneme or grapheme-to-phoneme (phoneme a term used by linguists to describe distinctive sounds in language). The simplest approach to converting text to phoneme is a dictionary approach in which the program stores a large dictionary containing all the words of the language and their correct pronunciation. Determining the correct pronunciation of each word is a matter of finding each word in the dictionary and replacing the spelling with the pronunciation specified in the dictionary. Another approach is based on rules in which pronunciation rules are applied to words to determine their pronunciation based on their spelling. It's like sounding, or synthetic acoustics, an approach to learning to read. Each approach has its advantages and disadvantages. The dictionary approach is quick and accurate, but it completely fails if it is given a word that is not in his vocabulary. As the size of the dictionary grows, so do the requirements for the memory space of the synthesis system. On the other hand, a rules-based approach works on any input, but the complexity of the rules increases significantly as the system takes into account irregular spelling or pronunciation. (Consider that the word is very common in English, but it is the only word in which the letter f is pronounced as v.) As a result, almost all speech synthesis systems use a combination of these approaches. Languages with phonemic spelling have a very regular writing system, and predicting the pronunciation of words based on their spelling is quite successful. Speech synthesis systems for such languages often use the rule-based method extensively, using dictionaries only for those few words, such as foreign names and borrowings, whose pronunciation is not obvious from their spelling. On the other hand, speech synthesis systems for languages such as English, which have highly irregular spelling systems, are more likely to rely on dictionaries and use rule-based methods only for unusual words or words that are not in their dictionaries. The problems of assessing consistent evaluation of speech synthesis systems may be difficult due to the lack of generally recognized objective evaluation criteria. Different organizations often use different speech data. The quality of speech synthesis systems also depends on the quality of production techniques (which can include analog or digital recording) and on the capabilities used to reproduce speech. Evaluation of speech synthesis systems has differences between production methods and object reproduction have often been compromised. Since 2005, however, some researchers have begun to evaluate speech synthesis systems using a common set of speeches. Prosodics and Emotional Content See also: Prosody () Study in the journal Speech Communication by Amy Drahota and colleagues from the University of Portsmouth, UK, reported that listeners of voice recordings can determine, at higher than random levels, whether the speaker is smiling. It was suggested that the identification of vocal functions that signal emotional content could be used to help make synthesized speech more natural. One of the related issues is to change the contour of the sentence depending on whether it is affirmative, interrogating or exclamation point. One method for step change is to use discrete cosine conversion in the source domain (linear residual prediction). Such methods of synchronous step modification step is necessary a priori step marking the synthesis of the speech database using methods such as the era of extraction using a dynamic index of the ploy applied on the integrated linear prediction of residual voiced speech regions. Dedicated equipment Icophone General Instrument SP0256-AL2 National Semiconductor DT1050 Digitalker (Mozer - Forrest Mozer) Texas Instruments LPC Speech Chips (Popular Systems Hardware Systems, Offering speech synthesis as a built-in feature. Mattel's Mattel Intellivision game console offered the Intellivoice Voice Synthesis module in 1982. It included the SP0256 Narrator synthesizer speech chip on a removable cartridge. and it was used to store a database of common words that could be combined to make phrases in Intellivision games. Because the Orator chip can also receive speech information from external memory, any additional words or phrases required can be stored inside the cartridge itself. The data consisted of lines of analog-filter coefficients to change the of the synthetic vocal tract chip model, rather than simple digitized samples. SAM Demo SAM on C64 Also released in 1982, Automatic Mouth Software was the first commercial all voice synthesis software. It was later used as the basis for Macintalk. The program was available for Apple computers, not including Apple II and Lisa, various Atari and Commodore 64 models. Apple preferred additional hardware containing DACs, although it could instead use a single-thread audio output of the computer (with the addition of a large distortion) if the card was not present. Atari used AUDIO chip POKEY. Speech playback on Atari usually turns off to interrupt requests and turn off the ANTIC chip during vocal output. Teh Teh exit is extremely distorted speech when the screen is on. The Commodore 64 used a built-in audio chip SID 64. Atari May have the first speech system integrated into the operating system were 1400XL/1450XL personal computers developed by Atari, Inc. using the Votrax SC01 chip in 1983. 1400XL/1450XL computers used the Finite State Machine to synthesize text to the speech of the world English Spelling. Unfortunately, 1400XL/1450XL personal computers were never delivered in quantity. Atari ST computers were sold from stspeech.tos on the floppy disk. Apple's first speech system integrated into the operating system that comes in quantity was Apple Computer's MacInTalk. The software was licensed by third-party developers Joseph Katz and Mark Barton (later SoftVoice, Inc.) and was introduced during the introduction of the Macintosh computer in 1984. In January of this year, the demo required 512 kilobytes of RAM memory. As a result, it could not work in the 128 kilobyte of RAM the first Mac actually comes with. In the early 1990s, Apple expanded its capabilities by offering a system of broad support for text-to-speech. With the introduction of faster PowerPC-based computers, they included a higher quality voice sample. Apple also introduced speech recognition into its systems, which provided a smooth set of commands. More recently, Apple has added voice-based samples. Starting out as a curiosity, Apple's Macintosh speech system has evolved into a fully supported PlainTalk program for people with vision problems. VoiceOver was first shown in 2005 in Mac OS X Tiger (10.4). During 10.4 (Tiger) and the first releases 10.5 (Leopard) there was only one standard voice delivery with Mac OS X. Starting from 10.6 (Snow Leopard), the user can choose from a wide list of multiple voices. VoiceOver voices have taken realistic-sounding breaths between offers, as well as improved clarity on high reading rates over PlainTalk. Mac OS X also includes, say, a command app that converts text into audible speech. The AppleScript Standard supplement includes a say verb that allows the script to use any of the established voices and control the pitch, speech speed and modulation of the conversational text. Apple's iOS operating system, used on iPhone, iPad and iPod Touch, uses VoiceOver speech synthesis for availability. Some third-party applications also provide speech synthesis to facilitate navigation, web browsing, or text translation. Amazon is used in Alexa and as software as a service in AWS (from 2017). AmigaOS Synthesis Example with included Say utility in Workbench 1.3 Second operating system to show advanced speech synthesis speech was AmigaOS, introduced in 1985. Voice Synthesis was licensed by Commodore International from SoftVoice, Inc., which also developed the original macinTalk system for text speaking. It featured a complete voice emulation system for American English, with male and female voices and stress markers, made possible by Amiga's audio chipset. The synthesis system was divided into a library of translators, which transformed unlimited English text into a standard set of phonetic codes and a narrator's device that introduced a formative speech generation model. AmigaOS also showed a high-level Speak Handler that allowed command-line users to redirect text output to speech. Speech synthesis has sometimes been used in third-party programs, such as word processors and educational software. The synthesis software remained largely unchanged with the first release of AmigaOS and Commodore eventually removed speech synthesis support from AmigaOS 2.1 forward. Despite the limitations of the American English phoneme, an unofficial version with a multilingual synthesis of speech was developed. It used an extended version of the translator library, which could translate into several languages, taking into account a set of rules for each language. Microsoft Windows See also: Modern Windows desktop systems can use SAPI 4 and SAPI 5 components to support speech synthesis and speech recognition. SAPI 4.0 was available as an additional supplement for Windows 95 and Windows 98. Windows 2000 added a narrator, a text-to-speech utility for people who have visual impairments. Third-party programs such as JAWS for Windows, Window-Eyes, Unseeded desktop access, Supernova and access to the system can perform a variety of tasks such as reading text aloud from a specified website, email account, text document, Windows clipboard, user keyboard input, etc. Some programs may use plug-ins, extensions, or add-ons to read the text aloud. Third-party programs are available that can read text from the system clipboard. is a server package for synthesis and voice recognition. It is designed for networking with web applications and call centers. Texas Instruments TI-99/4A TI-99/4A demo speech using a built-in dictionary In the early 1980s TI was known as a pioneer in speech synthesis, and a very popular speech synthesizer module was available for TI-99/4 and 4A. Speech synthesizers were offered for free with the purchase of a number of cartridges and were used by many TI-written video games (notable titles offered with speech during this promotion were Alpiner and Parsec). Synthesizer the linear predictive coding option has a small built-in vocabulary. The initial goal was to release small cartridges that are connected directly to the synth synthesizer that will increase the device's built-in vocabulary. However, the success of text-to-speech software in the Terminal Emulator II cartridge canceled the plan. Text-to-Speech (TTS) text systems relate to the ability of computers to read text aloud. TTS Engine converts written text into a phonemic view and then converts a phonemic view into wave shapes that can be output as sound. TTS engines with different languages, dialects and specialized dictionaries are available through third-party publishers. Android version 1.6 Android has added support for speech synthesis (TTS). Internet Currently has a number of applications, plug-ins and gadgets that can read messages directly from a customer's email and web pages from a web browser or Google toolbar. Some specialized programs can tell RSS feeds. On the one hand, online storytellers make it easier to deliver information by allowing users to listen to their favorite news sources and convert them into podcasts. On the other hand, online RSS readers are available on almost any computer connected to the Internet. Users can download generated audio files to portable devices, such as a podcast receiver, and listen to them while walking, jogging or commuting to work. A growing area of the Internet based on TTS is web assistance technology, such as Browsealoud from a British company and Readspeaker. It can provide TTS functionality to anyone (for reasons of accessibility, convenience, entertainment, or information) with access to a web browser. The non-profit Pediaphon project was created in 2006 to provide a similar TTS web interface to Wikipedia. Other work is being done in the context of W3C through the W3C Audio Incubator Group with the participation of the BBC and Google Inc. Open source open source software systems such as: Festival Speech Synthesis System, which uses synthesis based on diphons, as well as more modern and better-sounding methods. eSpeak, which supports a wide range of languages. gnuspeech, which uses articulation fusion from the Foundation. Others, after the commercial hardware failure of Intellivoice, game developers sparingly used software synthesis in later games. Earlier Systems from Atari, such as the Atari 5200 (Baseball) and Atari 2600 (Quadrun and Open Sesame), also had games that use software synthesis. (quote is needed) Some e-book readers such as Amazon Kindle, Samsung E6, PocketBook eReader Pro, enTourage eDGe and Bebook Neo. BBC Micro included Texas Instruments TMS5220 synthesis speech chip, Some models of Texas Home Computer Tools produced in 1979 and 1981 (Texas TI Instruments-99/4 and TI-99/4A) were capable of texting synthesis or read full words and phrases (text to dictionary) using very popular speech speech Peripheral. TI used its own codec to embed full spoken phrases into apps, primarily video games. IBM's 2 Warp 4 included VoiceType, a precursor to IBM ViaVoice. GPS Navigation devices manufactured by Garmin, Magellan, TomTom and others use speech synthesis for automotive navigation. Yamaha released a musical synthesizer in 1999, the Yamaha FS1R, which included the possibility of synthesis of Formant. Sequences of up to 512 individual vowels and consonants can be stored and reproduced, allowing short vocal phrases to be synthesized. Digital Sound Analogs Since the 2016 introduction of Adobe Voco audio editing and software prototype generation is slated to be part of adobe's Creative Suite and similarly included DeepMind WaveNet, a deep neural network based on audio synthesis software from Google 75 speech synthesis is on the verge of being completely indistinguishable from real human voice. Adobe Voco takes about 20 minutes of speech desired goal and after that it can generate a sound-like voice even phonemes that were not present in the training material. Software creates ethical problems because it allows you to steal the voices of other peoples and manipulate them to say something desired. At the 2018 Conference on Neural Information Processing Systems (NeurIPS), Researchers at Google presented the work Transfer of Learning from Speaker Testing to Multi-Taught Text Synthesis to Speech, which transfers learning from speaker testing to synthesis of text to speech, which can sound almost like any of the speech samples in just 5 seconds (listen). Researchers from Baidu Research also presented a voice cloning system with similar goals at the NeurIPS conference in 2018, although the result is rather inconclusive. I can't. By 2019, digital sonic criminals have found their way into the hands of criminals, as Symantec researchers are aware of three cases where digital sound technology has been used for crime. This increases the burden on the misinformation situation, coupled with the fact that the synthesis of the human image since the early 2000s has improved beyond the human inability to tell the real person, pictured with a real camera from a of a person depicted with a camera simulation. In 2016, 2D fake techniques were introduced that allow real-time forgery of the miki in the existing 2D video. In SIGGRAPH 2017, researchers from the University of Washington presented a digital twin of 's upper torso. (view) It was driven only by the voice track, as the original data for animation after the training phase to obtain lip sync and wider information about faces from instructional materials consisting of 2D video with was completed. In March 2020, a free web app that generates high-quality voices from the range of assortment characters from various media sources called 15.ai was released. Initial characters included GLaDOS from Portal, Twilight Sparkle and Fluttershy from My Little Pony: Friendship Is Magic and Tenth Doctor from Doctor Who. Subsequent updates included Wheatley from Portal 2, Soldier from Team Fortress 2 and the remaining main line-up of My Little Pony: Friendship Is Magic. A number of marking languages have been created to convey text-like speech in an XML-compatible format. The last of these is the Speech Synthesis Marking Language (SSML), which became a recommendation of W3C in 2004. Old speech synthesis marking languages include Java Speech Markup Language (JSML) and SABLE. Although each was proposed as a standard, none of them was widely accepted. Speech synthesis marking languages differ from language markings of dialogue. VoiceXML, for example, includes tags related to speech recognition, dialogue management, and touch set, in addition to text-to-speech markups. Speech synthesis applications have long been a vital auxiliary technological tool, and its application in this area is significant and widespread. This removes environmental barriers for people with a wide range of disabilities. The longest application has been in the use of screen readers for visually impaired people, but the text-to-speech system is now widely used by people with dyslexia and other reading difficulties, as well as pre- literate children. They are also often used to assist those with severe speech impairments, usually through special voice-out communication assistance. Speech synthesis techniques are also used in entertainment productions such as games and animations. In 2007, Animo Limited announced the development of a suite of software applications based on FineSpeech speech synthesis software, clearly customer-focused in the entertainment industry, capable of generating narrative and dialogue lines according to user specifications. The app reached maturity in 2008 when NEC Biglobe announced a web service that allows users to create phrases from the voices of Code Geass: Lelouch of the Rebellion R2. In recent years, text-to-speech for disabled and disabled media has become widely deployed in Mass Transit. Text-to-speech also finds new apps outside the disability market. For example, speech synthesis, combined with speech recognition, allows you to interact with mobile devices using natural language processing interfaces. Speech text is also used in the purchase of a second language. Voki, for example, is an educational tool created by Oddcast that allows users to create their own talking avatar using different accents. They can be e-mail, built into websites or distributed on social networks. In B Speech synthesis is a valuable computational help to analyze and evaluate speech disorders. The voice quality synthesizer, developed by Jorge C. Lucero et al at the University of Brasilia, simulates the physics of phonation and includes models of vocal frequency of fright and tremor, airflow noise and laryngal asymmetry. The synthesizer was used to simulate the timbre of dysphonic speakers with a controlled level of roughness, shortness of breath and tension. Stephen Hawking was one of the most famous people using the speech computer to communicate Watch also the Chinese synthesis of speech Comparison screen readers Comparison of speech synthesizers Euphonia (device) Orca (assistant technology) Paperless office speech processing device Silent speech interface Text speech to speech in digital television Links Allen, Jonathan; Junnikutt, M. Sharon; Clott, Dennis (1987). From text to speech: MITalk system. Cambridge University Press. ISBN 978-0-521-30641-6. Rubin, P.; Baer, T.; Merzmelstein. Articulation synthesizer for the perception of research. In the Journal of the American Acoustic Society. 70 (2): 321–328. 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