Text-to-Speech Synthesis

Machines can be an invaluable support in the latter case: Astro-physician Stephen Hawking gives all his lectures in this way. The aforementioned Telephone Relay Service is another example. The market for speech synthesis for blind users of personal computers will soon be invaded by mass-market synthesisers bundled with sound cards.

Talking books and toys. The toy market has already been touched by speech synthesis. Many speaking toys have appeared, under the impulse of the innovative 'Magic Spell' from Texas Instruments. The poor quality available inevitably restrains the educational ambition of such products. High Quality synthesis at affordable prices might well change this. In some cases, oral information is more efficient than written messages. The appeal is stronger, while the attention may still focus on other visual sources of information.

Hence the idea of incorporating speech synthesizers in measurement or control systems. In the long run, the development of high quality TTS systems is a necessary step as is the enhancement of speech recognizers towards more complete means of communication between men and computers. Multimedia is a first but promising move in this direction. Fundamental and applied research. TTS synthesizers possess a very peculiar feature which makes them wonderful laboratory tools for linguists: Consequently, they allow to investigate the efficiency of intonative and rhythmic models.

A particular type of TTS systems, which are based on a description of the vocal tract through its resonant frequencies its formants and denoted as formant synthesizers , has also been extensively used by phoneticians to study speech in terms of acoustical rules. In this manner, for instance, articulatory constraints have been enlightened and formally described. From now on, it should be clear that a reading machine would hardly adopt a processing scheme as the one naturally taken up by humans, whether it was for language analysis or for speech production itself.

Vocal sounds are inherently governed by the partial differential equations of fluid mechanics, applied in a dynamic case since our lung pressure, glottis tension, and vocal and nasal tracts configuration evolve with time. These are controlled by our cortex, which takes advantage of the power of its parallel structure to extract the essence of the text read: Even though, in the current state of the engineering art, building a Text-To-Speech synthesizer on such intricate models is almost scientifically conceivable intensive research on articulatory synthesis, neural networks, and semantic analysis give evidence of it , it would result anyway in a machine with a very high degree of possibly avoidable complexity, which is not always compatible with economical criteria.

After all, flies do not flap their wings! Figure 1 introduces the functional diagram of a very general TTS synthesizer. As for human reading, it comprises a Natural Language Processing module NLP , capable of producing a phonetic transcription of the text read, together with the desired intonation and rhythm often termed as prosody , and a Digital Signal Processing module DSP , which transforms the symbolic information it receives into speech.

But the formalisms and algorithms applied often manage, thanks to a judicious use of mathematical and linguistic knowledge of developers, to short-circuit certain processing steps. This is occasionally achieved at the expense of some restrictions on the text to pronounce, or results in some reduction of the "emotional dynamics" of the synthetic voice at least in comparison with human performances , but it generally allows to solve the problem in real time with limited memory requirements.

One immediately notices that, in addition with the expected letter-to-sound and prosody generation blocks, it comprises a morpho-syntactic analyser, underlying the need for some syntactic processing in a high quality Text-To-Speech system. Indeed, being able to reduce a given sentence into something like the sequence of its parts-of-speech, and to further describe it in the form of a syntax tree, which unveils its internal structure, is required for at least two reasons:.

A pre-processing module, which organizes the input sentences into manageable lists of words. It identifies numbers, abbreviations, acronyms and idiomatics and transforms them into full text when needed. An important problem is encountered as soon as the character level: It can be solved, to some extent, with elementary regular grammars. A morphological analysis module, the task of which is to propose all possible part of speech categories for each word taken individually, on the basis of their spelling.

Inflected, derived, and compound words are decomposed into their elementery graphemic units their morphs by simple regular grammars exploiting lexicons of stems and affixes see the CNET TTS conversion program for French [Larreur et al. The contextual analysis module considers words in their context, which allows it to reduce the list of their possible part of speech categories to a very restricted number of highly probable hypotheses, given the corresponding possible parts of speech of neighbouring words.

eSpeak text to speech

Finally, a syntactic-prosodic parser, which examines the remaining search space and finds the text structure i. A poem of the Dutch high school teacher and linguist G. Trenite surveys this problem in an amusing way. It desperately ends with:. Finally, which rimes with "enough", Though, through, plough, cough, hough, or tough? Hiccough has the sound of "cup", My advice is The Letter-To-Sound LTS module is responsible for the automatic determination of the phonetic transcription of the incoming text.

It thus seems, at first sight, that its task is as simple as performing the equivalent of a dictionary look-up! From a deeper examination, however, one quickly realizes that most words appear in genuine speech with several phonetic transcriptions, many of which are not even mentioned in pronunciation dictionaries.

Clearly, points 1 and 2 heavily rely on a preliminary morphosyntactic and possibly semantic analysis of the sentences to read.

To a lesser extent, it also happens to be the case for point 3 as well, since reduction processes are not only a matter of context-sensitive phonation, but they also rely on morphological structure and on word grouping, that is on morphosyntax. It is then possible to organize the task of the LTS module in many ways Fig. Dictionary-based solutions consist of storing a maximum of phonological knowledge into a lexicon.

In order to keep its size reasonably small, entries are generally restricted to morphemes, and the pronunciation of surface forms is accounted for by inflectional, derivational, and compounding morphophonemic rules which describe how the phonetic transcriptions of their morphemic constituents are modified when they are combined into words. Morphemes that cannot be found in the lexicon are transcribed by rule.

After a first phonemic transcription of each word has been obtained, some phonetic post-processing is generally applied, so as to account for coarticulatory smoothing phenomena. A rather different strategy is adopted in rule-based transcription systems, which transfer most of the phonological competence of dictionaries into a set of letter-to-sound or grapheme-to-phoneme rules.

This time, only those words that are pronounced in such a particular way that they constitute a rule on their own are stored in an exceptions dictionary. Notice that, since many exceptions are found in the most frequent words, a reasonably small exceptions dictionary can account for a large fraction of the words in a running text. It has been argued in the early days of powerful dictionary-based methods that they were inherently capable of achieving higher accuracy than letter-to-sound rules [Coker et al 90], given the availability of very large phonetic dictionaries on computers.

Clearly, some trade-off is inescapable. Besides, the compromise is language-dependent, given the obvious differences in the reliability of letter-to-sound correspondences for different languages. The term prosody refers to certain properties of the speech signal which are related to audible changes in pitch, loudness, syllable length. Prosodic features have specific functions in speech communication see Fig. The most apparent effect of prosody is that of focus. For instance, there are certain pitch events which make a syllable stand out within the utterance, and indirectly the word or syntactic group it belongs to will be highlighted as an important or new component in the meaning of that utterance.

The presence of a focus marking may have various effects, such as contrast, depending on the place where it occurs, or the semantic context of the utterance. Different kinds of information provided by intonation lines indicate pitch movements; solid lines indicate stress. Relationships between words saw-yesterday; I-yesterday; I-him c.

Speech Synthesis Demo

Finality top or continuation bottom , as it appears on the last syllable; d. Segmentation of the sentence into groups of syllables. Although maybe less obvious, there are other, more systematic or general functions.

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Prosodic features create a segmentation of the speech chain into groups of syllables, or, put the other way round, they give rise to the grouping of syllables and words into larger chunks. Moreover, there are prosodic features which indicate relationships between such groups, indicating that two or more groups of syllables are linked in some way. This grouping effect is hierarchical, although not necessarily identical to the syntactic structuring of the utterance. Does this mean that TTS systems are doomed to a mere robot-like intonation until a brilliant computational linguist announces a working semantic-pragmatic analyzer for unrestricted text i.

There are various reasons to think not, provided one accepts an important restriction on the naturalness of the synthetic voice, i. Neutral intonation does not express unusual emphasis, contrastive stress or stylistic effects: This approach removes the necessity for reference to context or world knowledge while retaining ambitious linguistic goals.

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The key idea is that the "correct" syntactic structure, the one that precisely requires some semantic and pragmatic insight, is not essential for producing such a prosody [see also O'Shaughnessy 90]. With these considerations in mind, it is not surprising that commercially developed TTS system have emphasized coverage rather than linguistic sophistication, by concentrating their efforts on text analysis strategies aimed to segment the surface structure of incoming sentences, as opposed to their syntactically, semantically, and pragmatically related deep structure. The resulting syntactic-prosodic descriptions organize sentences in terms of prosodic groups strongly related to phrases and therefore also termed as minor or intermediate phrases , but with a very limited amount of embedding, typically a single level for these minor phrases as parts of higher-order prosodic phrases also termed as major or intonational phrases, which can be seen as a prosodic-syntactic equivalent for clauses and a second one for these major phrases as parts of sentences, to the extent that the related major phrase boundaries can be safely obtained from relatively simple text analysis methods.

In other words, they focus on obtaining an acceptable segmentation and translate it into the continuation or finality marks of Fig. Liberman and Church [], for instance, have recently reported on such a very crude algorithm, termed as the chinks 'n chunks algorithm, in which prosodic phrases which they call f-groups are accounted for by the simple regular rule:. They show that this approach produces efficient grouping in most cases, slightly better actually than the simpler decomposition into sequences of function and content words, as shown in the example below:.

Once the syntactic-prosodic structure of a sentence has been derived, it is used to obtain the precise duration of each phoneme and of silences , as well as the intonation to apply on them. This last step, however, is not straightforward either. It requires to formalize a lot of phonetic or phonological knowledge, either obtained from experts or automatically acquired from data with statistical methods. More information on this can be found in [Dutoit 96].

Intuitively, the operations involved in the DSP module are the computer analogue of dynamically controlling the articulatory muscles and the vibratory frequency of the vocal folds so that the output signal matches the input requirements. In order to do it properly, the DSP module should obviously, in some way, take articulatory constraints into account, since it has been known for a long time that phonetic transitions are more important than stable states for the understanding of speech [Libermann 59]. This, in turn, can be basically achieved in two ways:.

Two main classes of TTS systems have emerged from this alternative, which quickly turned into synthesis philosophies given the divergences they present in their means and objectives: Rule-based synthesizers are mostly in favour with phoneticians and phonologists, as they constitute a cognitive, generative approach of the phonation mechanism. The broad spreading of the Klatt synthesizer [Klatt 80], for instance, is principally due to its invaluable assistance in the study of the characteristics of natural speech, by analytic listening of rule-synthesized speech.

What is more, the existence of relationships between articulatory parameters and the inputs of the Klatt model make it a practical tool for investigating physiological constraints [Stevens 90]. For historical and practical reasons mainly the need for a physical interpretability of the model , rule synthesizers always appear in the form of formant synthesizers.

These describe speech as the dynamic evolution of up to 60 parameters [Stevens 90], mostly related to formant and anti-formant frequencies and bandwidths together with glottal waveforms. Clearly, the large number of coupled parameters complicates the analysis stage and tends to produce analysis errors. What is more, formant frequencies and bandwidths are inherently difficult to estimate from speech data.

The need for intensive trials and errors in order to cope with analysis errors, makes them time-consuming systems to develop several years are commonplace. Yet, the synthesis quality achieved up to now reveals typical buzzyness problems, which originate from the rules themselves: Rule-based synthesizers remain, however, a potentially powerful approach to speech synthesis. They allow, for instance, to study speaker-dependent voice features so that switching from one synthetic voice into another can be achieved with the help of specialized rules in the rule database.

Following the same idea, synthesis-by-rule seems to be a natural way of handling the articulatory aspects of changes in speaking styles as opposed to their prosodic counterpart, which can be accounted for by concatenation-based synthesizers as well. S system [O'Shaughnessy 84] for French. As opposed to rule-based ones, concatenative synthesizers possess a very limited knowledge of the data they handle: This clearly appears in figure 6, where all the operations that could indifferently be used in the context of a music synthesizer i.

A series of preliminary stages have to be fulfilled before the synthesizer can produce its first utterance. At first, segments are chosen so as to minimize future concatenation problems. Formant synthesis does not use human speech samples at runtime. Instead, the synthesized speech output is created using additive synthesis and an acoustic model physical modelling synthesis.

This method is sometimes called rules-based synthesis ; however, many concatenative systems also have rules-based components. Many systems based on formant synthesis technology generate artificial, robotic-sounding speech that would never be mistaken for human speech. However, maximum naturalness is not always the goal of a speech synthesis system, and formant synthesis systems have advantages over concatenative systems. Formant-synthesized speech can be reliably intelligible, even at very high speeds, avoiding the acoustic glitches that commonly plague concatenative systems.

High-speed synthesized speech is used by the visually impaired to quickly navigate computers using a screen reader. Formant synthesizers are usually smaller programs than concatenative systems because they do not have a database of speech samples. They can therefore be used in embedded systems , where memory and microprocessor power are especially limited. Because formant-based systems have complete control of all aspects of the output speech, a wide variety of prosodies and intonations can be output, conveying not just questions and statements, but a variety of emotions and tones of voice.

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Creating proper intonation for these projects was painstaking, and the results have yet to be matched by real-time text-to-speech interfaces. Formant synthesis was implemented in hardware in the Yamaha FS1R synthesizer, but the speech aspect of formants was never realized in the synthesis. It was capable of short, several-second formant sequences which could speak a single phrase, but since the MIDI control interface was so restrictive live speech was an impossibility.

Articulatory synthesis refers to computational techniques for synthesizing speech based on models of the human vocal tract and the articulation processes occurring there. The first articulatory synthesizer regularly used for laboratory experiments was developed at Haskins Laboratories in the mids by Philip Rubin , Tom Baer, and Paul Mermelstein. Until recently, articulatory synthesis models have not been incorporated into commercial speech synthesis systems. A notable exception is the NeXT -based system originally developed and marketed by Trillium Sound Research, a spin-off company of the University of Calgary , where much of the original research was conducted.

More recent synthesizers, developed by Jorge C. Lucero and colleagues, incorporate models of vocal fold biomechanics, glottal aerodynamics and acoustic wave propagation in the bronqui, traquea, nasal and oral cavities, and thus constitute full systems of physics-based speech simulation. HMM-based synthesis is a synthesis method based on hidden Markov models , also called Statistical Parametric Synthesis.

In this system, the frequency spectrum vocal tract , fundamental frequency voice source , and duration prosody of speech are modeled simultaneously by HMMs. Speech waveforms are generated from HMMs themselves based on the maximum likelihood criterion. Sinewave synthesis is a technique for synthesizing speech by replacing the formants main bands of energy with pure tone whistles. Some DNN-based speech synthesizers are approaching the quality of the human voice.

The process of normalizing text is rarely straightforward. Texts are full of heteronyms , numbers , and abbreviations that all require expansion into a phonetic representation. There are many spellings in English which are pronounced differently based on context. For example, "My latest project is to learn how to better project my voice" contains two pronunciations of "project". Most text-to-speech TTS systems do not generate semantic representations of their input texts, as processes for doing so are unreliable, poorly understood, and computationally ineffective.

As a result, various heuristic techniques are used to guess the proper way to disambiguate homographs , like examining neighboring words and using statistics about frequency of occurrence. Recently TTS systems have begun to use HMMs discussed above to generate " parts of speech " to aid in disambiguating homographs.

This technique is quite successful for many cases such as whether "read" should be pronounced as "red" implying past tense, or as "reed" implying present tense. Typical error rates when using HMMs in this fashion are usually below five percent. These techniques also work well for most European languages, although access to required training corpora is frequently difficult in these languages. Deciding how to convert numbers is another problem that TTS systems have to address. It is a simple programming challenge to convert a number into words at least in English , like "" becoming "one thousand three hundred twenty-five.

A TTS system can often infer how to expand a number based on surrounding words, numbers, and punctuation, and sometimes the system provides a way to specify the context if it is ambiguous. Similarly, abbreviations can be ambiguous. For example, the abbreviation "in" for "inches" must be differentiated from the word "in", and the address "12 St John St. TTS systems with intelligent front ends can make educated guesses about ambiguous abbreviations, while others provide the same result in all cases, resulting in nonsensical and sometimes comical outputs, such as "co-operation" being rendered as "company operation".

Speech synthesis systems use two basic approaches to determine the pronunciation of a word based on its spelling , a process which is often called text-to-phoneme or grapheme -to-phoneme conversion phoneme is the term used by linguists to describe distinctive sounds in a language. The simplest approach to text-to-phoneme conversion is the dictionary-based approach, where a large dictionary containing all the words of a language and their correct pronunciations is stored by the program. Determining the correct pronunciation of each word is a matter of looking up each word in the dictionary and replacing the spelling with the pronunciation specified in the dictionary.

The other approach is rule-based, in which pronunciation rules are applied to words to determine their pronunciations based on their spellings. This is similar to the "sounding out", or synthetic phonics , approach to learning reading. Each approach has advantages and drawbacks.

The dictionary-based approach is quick and accurate, but completely fails if it is given a word which is not in its dictionary. As dictionary size grows, so too does the memory space requirements of the synthesis system. On the other hand, the rule-based approach works on any input, but the complexity of the rules grows substantially as the system takes into account irregular spellings or pronunciations.

Consider that the word "of" is very common in English, yet is the only word in which the letter "f" is pronounced [v]. As a result, nearly all speech synthesis systems use a combination of these approaches. Languages with a phonemic orthography have a very regular writing system, and the prediction of the pronunciation of words based on their spellings is quite successful. Speech synthesis systems for such languages often use the rule-based method extensively, resorting to dictionaries only for those few words, like foreign names and borrowings , whose pronunciations are not obvious from their spellings.

On the other hand, speech synthesis systems for languages like English , which have extremely irregular spelling systems, are more likely to rely on dictionaries, and to use rule-based methods only for unusual words, or words that aren't in their dictionaries. The consistent evaluation of speech synthesis systems may be difficult because of a lack of universally agreed objective evaluation criteria.

Different organizations often use different speech data. The quality of speech synthesis systems also depends on the quality of the production technique which may involve analogue or digital recording and on the facilities used to replay the speech. Evaluating speech synthesis systems has therefore often been compromised by differences between production techniques and replay facilities. Since , however, some researchers have started to evaluate speech synthesis systems using a common speech dataset.

A study in the journal Speech Communication by Amy Drahota and colleagues at the University of Portsmouth , UK , reported that listeners to voice recordings could determine, at better than chance levels, whether or not the speaker was smiling. One of the related issues is modification of the pitch contour of the sentence, depending upon whether it is an affirmative, interrogative or exclamatory sentence.

One of the techniques for pitch modification [43] uses discrete cosine transform in the source domain linear prediction residual. Such pitch synchronous pitch modification techniques need a priori pitch marking of the synthesis speech database using techniques such as epoch extraction using dynamic plosion index applied on the integrated linear prediction residual of the voiced regions of speech. It included the SP Narrator speech synthesizer chip on a removable cartridge.

The Narrator had 2kB of Read-Only Memory ROM , and this was utilized to store a database of generic words that could be combined to make phrases in Intellivision games. Since the Orator chip could also accept speech data from external memory, any additional words or phrases needed could be stored inside the cartridge itself. The data consisted of strings of analog-filter coefficients to modify the behavior of the chip's synthetic vocal-tract model, rather than simple digitized samples.

Also released in , Software Automatic Mouth was the first commercial all-software voice synthesis program. It was later used as the basis for Macintalk. The Apple version preferred additional hardware that contained DACs, although it could instead use the computer's one-bit audio output with the addition of much distortion if the card was not present.

The audible output is extremely distorted speech when the screen is on. The Commodore 64 made use of the 64's embedded SID audio chip. The Atari ST computers were sold with "stspeech. The first speech system integrated into an operating system that shipped in quantity was Apple Computer 's MacInTalk. This January demo required kilobytes of RAM memory. As a result, it could not run in the kilobytes of RAM the first Mac actually shipped with. In the early s Apple expanded its capabilities offering system wide text-to-speech support.

With the introduction of faster PowerPC-based computers they included higher quality voice sampling. Apple also introduced speech recognition into its systems which provided a fluid command set. More recently, Apple has added sample-based voices. Starting as a curiosity, the speech system of Apple Macintosh has evolved into a fully supported program, PlainTalk , for people with vision problems. VoiceOver voices feature the taking of realistic-sounding breaths between sentences, as well as improved clarity at high read rates over PlainTalk.

Mac OS X also includes say , a command-line based application that converts text to audible speech. The AppleScript Standard Additions includes a say verb that allows a script to use any of the installed voices and to control the pitch, speaking rate and modulation of the spoken text. The second operating system to feature advanced speech synthesis capabilities was AmigaOS , introduced in It featured a complete system of voice emulation for American English, with both male and female voices and "stress" indicator markers, made possible through the Amiga 's audio chipset.

AmigaOS also featured a high-level " Speak Handler ", which allowed command-line users to redirect text output to speech. Speech synthesis was occasionally used in third-party programs, particularly word processors and educational software. The synthesis software remained largely unchanged from the first AmigaOS release and Commodore eventually removed speech synthesis support from AmigaOS 2.

Despite the American English phoneme limitation, an unofficial version with multilingual speech synthesis was developed. This made use of an enhanced version of the translator library which could translate a number of languages, given a set of rules for each language. Windows added Narrator , a text—to—speech utility for people who have visual impairment. Third-party programs such as JAWS for Windows, Window-Eyes, Non-visual Desktop Access, Supernova and System Access can perform various text-to-speech tasks such as reading text aloud from a specified website, email account, text document, the Windows clipboard, the user's keyboard typing, etc.

Not all programs can use speech synthesis directly. Third-party programs are available that can read text from the system clipboard. Microsoft Speech Server is a server-based package for voice synthesis and recognition. It is designed for network use with web applications and call centers. Speech synthesizers were offered 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.

The synthesizer uses a variant of linear predictive coding and has a small in-built vocabulary. The original intent was to release small cartridges that plugged directly into the synthesizer unit, which would increase the device's built in vocabulary. However, the success of software text-to-speech in the Terminal Emulator II cartridge cancelled that plan.

Text-to-Speech TTS refers to the ability of computers to read text aloud. A TTS Engine converts written text to a phonemic representation, then converts the phonemic representation to waveforms that can be output as sound. TTS engines with different languages, dialects and specialized vocabularies are available through third-party publishers. Currently, there are a number of applications , plugins and gadgets that can read messages directly from an e-mail client and web pages from a web browser or Google Toolbar.

Some specialized software can narrate RSS-feeds. On one hand, online RSS-narrators simplify information delivery by allowing users to listen to their favourite news sources and to convert them to podcasts. Users can download generated audio files to portable devices, e. A growing field in Internet based TTS is web-based assistive technology , e. It can deliver TTS functionality to anyone for reasons of accessibility, convenience, entertainment or information with access to a web browser.

The non-profit project Pediaphon was created in to provide a similar web-based TTS interface to the Wikipedia. Some open-source software systems are available, such as:. With the introduction of Adobe Voco audio editing and generating software prototype slated to be part of the Adobe Creative Suite and the similarly enabled DeepMind WaveNet , a deep neural network based audio synthesis software from Google [59] speech synthesis is verging on being completely indistinguishable from a real human's voice.

Adobe Voco takes approximately 20 minutes of the desired target's speech and after that it can generate sound-alike voice with even phonemes that were not present in the training material. The software poses ethical concerns as it allows to steal other peoples voices and manipulate them to say anything desired. This increases the stress on the disinformation situation coupled with the facts that.

A number of markup languages have been established for the rendition of text as speech in an XML -compliant format. Although each of these was proposed as a standard, none of them have been widely adopted. Speech synthesis markup languages are distinguished from dialogue markup languages. VoiceXML , for example, includes tags related to speech recognition, dialogue management and touchtone dialing, in addition to text-to-speech markup. Speech synthesis has long been a vital assistive technology tool and its application in this area is significant and widespread.

It allows environmental barriers to be removed for people with a wide range of disabilities. The longest application has been in the use of screen readers for people with visual impairment , but text-to-speech systems are now commonly used by people with dyslexia and other reading difficulties as well as by pre-literate children.

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They are also frequently employed to aid those with severe speech impairment usually through a dedicated voice output communication aid. Speech synthesis techniques are also used in entertainment productions such as games and animations. In , Animo Limited announced the development of a software application package based on its speech synthesis software FineSpeech, explicitly geared towards customers in the entertainment industries, able to generate narration and lines of dialogue according to user specifications.

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