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Speech synthesis is the artificial production of human Speech . A system used for this purpose is termed a '''speech synthesizer''', and can be implemented in Software or Hardware . Speech synthesis systems are often called '''text-to-speech (TTS)''' systems in reference to their ability to convert text into speech. However, there exist systems that instead render Symbolic Linguistic Representation s like Phonetic Transcription s into speech. OVERVIEW OF SPEECH SYNTHESIS TECHNOLOGY A text-to-speech system (or engine) is composed of two parts: a '''front-end''' and a '''back-end'''. Broadly, the front-end takes Input in the form of text and Output s a Symbolic Linguistic Representation . The back-end takes the symbolic linguistic representation as input and outputs the synthesized speech Waveform . The Front-end has two major tasks. First it takes the raw text and converts things like numbers and abbreviations into their written-out word equivalents. This process is often called ''text normalization'', ''pre-processing'', or ''tokenization''. Then it assigns Phonetic Transcriptions to each word, and divides and marks the text into various Prosodic Units , like Phrase s, Clause s, and Sentence s. The process of assigning phonetic transcriptions to words is called ''text-to-phoneme (TTP)'' or '' Grapheme -to-phoneme (GTP)'' conversion. The combination of phonetic transcriptions and prosody information make up the ''symbolic linguistic representation'' output of the front end. The other part, the back-end, takes the symbolic linguistic representation and converts it into actual sound output. The back end is often referred to as the synthesizer. The different techniques synthesizers use are described below. Nahit HISTORY Long before modern electronic signal processing was invented, speech researchers tried to build machines to create human speech. Early examples of 'speaking heads' were made by Gerbert Of Aurillac (d. 1003), Albertus Magnus (1198–1280), and Roger Bacon (1214–1294). In 1779, the Danish scientist Christian Kratzenstein , working at that time at the Russian Academy Of Sciences , built models of the human vocal tract that could produce the five long Vowel sounds (a, e, i, o and u). This was followed by the bellows-operated 'Acoustic-Mechanical Speech Machine' by Wolfgang Von Kempelen of Vienna , Austria , described in his 1791 paper ''Mechanismus der menschlichen Sprache nebst der Beschreibung seiner sprechenden Maschine'' ("mechanism of the human speech with description of its speaking machine", J.B. Degen, Wien). This machine added models of the tongue and lips, enabling it to produce Consonant s as well as vowels. In 1837 Charles Wheatstone produced a 'speaking machine' based on von Kempelen's design, and in 1857 M. Faber built the 'Euphonia'. Wheatstone's design was resurrected in 1923 by Paget. 's VODER was exhibited at the 1939 New York World's Fair and produced clearly intelligible speech.]] In the 1930s, Bell Labs developed the VOCODER , a keyboard-operated electronic speech analyzer and synthesizer that was said to be clearly intelligible. Homer Dudley refined this device into the VODER, which he exhibited at the 1939 New York World's Fair . Early electronic speech synthesizers sounded very robotic and were often barely intelligible. However, the quality of synthesized speech has steadily improved, and output from contemporary speech synthesis systems is sometimes indistinguishable from actual human speech. Despite the success of purely electronic speech synthesis, research is still being conducted into mechanical speech synthesizers for use in humanoid Robot s. Even a perfect electronic synthesizer is limited by the quality of the transducer (usually a Loudspeaker ) that produces the sound, so in a robot a mechanical system may be able to produce a more natural sound than a small loudspeaker. The first computer-based speech synthesis systems were created in the late 1950s and the first complete text-to-speech system was completed in 1968. In 1961 physicist '' computer sings the same song as he is being put to sleep by astronaut Dave Bowman . Bell Labs: Where "HAL" First Spoke (Bell Labs Speech Synthesis website) Since then, there have been many advances in the technologies used to synthesize speech. See the External Links Section below for state-of-the-art commercial and free text-to-speech systems. References:
SYNTHESIZER TECHNOLOGIES The two characteristics used to describe the quality of a speech synthesis system are ''naturalness'' and ''intelligibility''. The ''naturalness'' of a speech synthesizer refers to how much the output sounds like the speech of a real person. The ''intelligibility'' of a speech synthesizer refers to how easily the output can be understood. The ideal speech synthesizer is both natural and intelligible, and each of the different synthesis technologies try to maximize both of these characteristics. Some of the technologies are better at naturalness or intelligibility and the goals of a synthesis system will often determine what approach is used. There are two main technologies used for the generating synthetic speech waveforms: concatenative synthesis and ''' Formant synthesis'''. Concatenative synthesis Concatenative synthesis is based on the concatenation (or stringing together) of segments of recorded speech. Generally, concatenative synthesis gives the most natural sounding synthesized speech. However, natural variation in speech and automated techniques for segmenting the waveforms sometimes result in audible glitches in the output, detracting from the naturalness. There are three main subtypes of concatenative synthesis. Unit selection synthesis Unit selection synthesis uses large speech s, Syllable s, Morpheme s, Word s, Phrase s, and Sentence s. Typically, the division into segments is done using a specially modified Speech Recognizer set to a "forced alignment" mode with some hand correction afterward, using visual representations such as the Waveform and Spectrogram . An Index of the units in the speech database is then created based on the segmentation and acoustic parameters like the Fundamental Frequency ( Pitch ), duration, position in the syllable, and neighboring phones. At Runtime , the desired target utterance is created by determining the best chain of candidate units from the database (unit selection). This process is typically achieved using a specially-weighted decision tree. Unit selection gives the greatest naturalness due to the fact that it does not apply a large amount of Digital Signal Processing to the recorded speech, which often makes recorded speech sound less natural, although some systems may use a small amount of signal processing at the point of concatenation to smooth the waveform. In fact, output from the best unit selection systems is often indistinguishable from real human voices, especially in contexts for which the TTS system has been tuned. However, maximum naturalness often requires unit selection speech databases to be very large, in some systems ranging into the gigabytes of recorded data and numbering into the dozens of hours of recorded speech. Diphone synthesis Diphone synthesis uses a minimal speech database containing all the of a sentence is superimposed on these minimal units by means of Digital Signal Processing techniques such as Linear Predictive Coding , PSOLA or MBROLA . The quality of the resulting speech is generally not as good as that from unit selection but more natural-sounding than the output of formant synthesizers. Diphone synthesis suffers from the sonic glitches of concatenative synthesis and the robotic-sounding nature of formant synthesis, and has few of the advantages of either approach other than small size. As such, its use in commercial applications is declining, although it continues to be used in research because there are a number of freely available implementations. Domain-specific synthesis Domain-specific synthesis concatenates pre-recorded words and phrases to create complete utterances. It is used in applications where the variety of texts the system will output is limited to a particular domain, like transit schedule announcements or weather reports. This technology is very simple to implement, and has been in commercial use for a long time: this is the technology used by Gadget s like talking clocks and calculators. The naturalness of these systems can potentially be very high because the variety of sentence types is limited and closely matches the prosody and intonation of the original recordings. However, because these systems are limited by the words and phrases in its database, they are not general-purpose and can only synthesize the combinations of words and phrases they have been pre-programmed with. Formant synthesis Formant synthesis does not use any human speech samples at runtime. Instead, the output synthesized speech is created using an acoustic model. Parameters such as Fundamental Frequency , Voicing , and Noise levels are varied over time to create a Waveform of artificial speech. This method is sometimes called rule-based synthesis, but some argue that because many concatenative systems use rule-based components for some parts of the system, like the front end, the term is not specific enough. Many systems based on formant synthesis technology generate artificial, robotic-sounding speech, and the output would never be mistaken for the speech of a real human. However, maximum naturalness is not always the goal of a speech synthesis system, and formant synthesis systems have some advantages over concatenative systems. Formant synthesized speech can be very reliably intelligible, even at very high speeds, avoiding the acoustic glitches that can often plague concatenative systems. High speed synthesized speech is often used by the visually impaired for quickly navigating computers using a Screen Reader . Second, formant synthesizers are often smaller programs than concatenative systems because they do not have a database of speech samples. They can thus be used in Embedded Computing situations where memory space and processor power are often scarce. Last, because formant-based systems have total control over all aspects of the output speech, a wide variety of prosody or Intonation can be output, conveying not just questions and statements, but a variety of emotions and tones of voice. Other synthesis methods
FRONT-END CHALLENGES Text normalization challenges The process of normalizing text is rarely straightforward. Texts are full of Homograph s, numbers and abbreviations that all ultimately require expansion into a phonetic representation. There are many words in English which are pronounced differently based on context. Some examples:
Most TTS systems do not generate semantic representations of their input texts, as processes for doing so are not reliable, well-understood, or computationally effective. As a result, various Heuristic techniques are used to guess the proper way to disambiguate homographs, like looking at neighboring words and using statistics about frequency of occurrence. Deciding how to convert numbers is another problem TTS systems have to address. It is a fairly simple programming challenge to convert a number into words, like 1325 becoming "one thousand three hundred twenty-five". However, numbers occur in many different contexts in texts, and 1325 should probably be read as "thirteen twenty-five" when part of an address (1325 Main St.) and as "one three two five" if it is the last four digits of a social security number. Often a TTS system can infer how to expand a number based on surrounding words, numbers, and punctuation, and sometimes the systems provide a way to specify the type of context if it is ambiguous. Similarly, abbreviations like "etc." are easily rendered as "et cetera", but often abbreviations can be ambiguous. For example, the abbreviation "'''in.'''" in the following example: "Yesterday it rained 3 in. Take 1 out, then put 3 in." "'''St.'''" can also be ambiguous: "St. John St." TTS systems with intelligent front ends can make educated guesses about how to deal with ambiguous abbreviations, while others do the same thing in all cases, resulting in nonsensical but sometimes comical outputs: "Yesterday it rained three in." or "Take one out, then put three inches." Text-to-phoneme challenges 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, as Phoneme is the term used by Linguist s 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 pronunciation 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 used for text-to-phoneme conversion is the rule-based approach, where rules for the pronunciations of words are applied to words to work out 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 has the advantages of being quick and accurate, but it completely fails if it is given a word which is not in its dictionary, and 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 it takes into account irregular spellings or pronunciations. As a result, nearly all speech synthesis systems use a combination of both approaches. Some languages, like Spanish , have a very regular writing system, and the prediction of the pronunciation of words based on the spelling works correctly in nearly all instances. Speech synthesis systems for languages like this often use the rule-based approach as the core approach for text-to-phoneme conversion, resorting to dictionaries only for those few words, like foreign names and borrowings, whose pronunciation is not obvious from the spelling. On the other hand, speech synthesis for languages like English , which have extremely irregular spelling systems, often rely mostly on dictionaries and use rule-based approaches only for unusual words or names that aren't in the dictionary. SPEECH SYNTHESIS MARKUP LANGUAGES A number of Markup Language s have been established for rendition of text as speech in an XML compliant format, the most recent being SSML proposed by the W3C which is in draft status at the time of this writing. Older speech synthesis markup languages include SABLE and JSML . Although each of these was proposed as a new standard, still none of them has been widely adopted. A subset of the Cascading Style Sheets 2 specification includes Aural Cascading Style Sheets . Speech synthesis markup languages should be distinguished from dialogue markup languages such as VoiceXML , which includes, in addition to text-to-speech markup, tags related to speech recognition, dialogue management and touchtone dialing. CITED REFERENCES SEE ALSO
EXTERNAL LINKS Misc
Freely available TTS systems
Commercially available TTS systems
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