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Speech synthesis

Speech synthesis is the generation of human speech without directly using a human voice.

Generally speaking, a speech synthesizer is software or hardware capable of rendering artificial speech.

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 can only render symbolic linguistic representations[?] like phonetic transcriptions into speech.

Table of contents

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 outputs a symbolic linguistic representation[?]. The back end takes the symbolic linguistic representation as input and outputs the synthesized speech waveform. The naturalness of a speech synthesizer usually refers to how much the output sounds like the speech of a real person.

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 phrases, clauses, and sentences. 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 information about prosodic units 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.

History Early speech synthesizers sounded very robotic and were often barely intelligible. Output from contemporary TTS systems are often indistinguishable from actual human speech.

TODO: see [Dennis Klatt's History of Speech Synthesis (http://www.cs.indiana.edu/rhythmsp/ASA/Contents)]

Synthesizer technologies

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 uses large speech databases (more than one hour of recorded speech). During database creation, each recorded utterance is segmented into some or all of the following: individual phones, syllables, morphemes, words, phrases, and sentences. The division into segments can be done using a number of techniques, like clustering, using a specially modified speech recognizer, or by hand, using visual representations of the waveform and spectragraphs. An index of the units in the speech database is then created based on the segmentation and acoustic parameters like the fundamental frequency (pitch). At runtime, the desired target utterance is created by determining the best chain of candidate units from the database (unit selection). This technique gives the greatest naturalness due to the fact that it does not apply digital signal processing techniques to the recorded speech, which often makes recorded speech sound less natural. In fact, output the best unit selection systems are 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 uses a minimal speech database containing all the Diphones[?] (sound-to-sound transitions) occurring in a given language. The number of diphones depends on the phonotactics of the language: Spanish has about 800 diphones, German about 2500. In diphone synthesis, only one example of each diphone is contained in the speech database. At runtime, the target prosody 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 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 things 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

  • Articulatory synthesis is a synthesis method mostly of academic interest at the moment. It is based on computational models of the human vocal tract[?] and the articulation processes occurring there. These models are currently not sufficiently advanced to be used in commercial speech synthesis systems.

  • Hybrid synthesis marries aspects of formant and concatenative synthesis to minimize the acoustic glitches when speech segments are concatenated.

Text-to-phoneme challenges TODO: rule-based vs. dictionary-based systems

Front End challenges The process of normalizing text is rarely straightforward. Texts are full of homographs, 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:

  • project: My latest project is to learn how to better project my voice.
  • bow: The girl with the bow in her hair was told to bow deeply when greeting her superiors.

Since most TTS systems do not generate semantic representations of their input texts, various techniques are used to guess the proper way to disambiguate homographs, like looking at neighboring words and using statistics about frequency of occurence.

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."

Examples of current systems

Some freely available text-to-speech systems:

  • [Festival (http://www.cstr.ed.ac.uk/projects/festival/)] is a freely available complete diphone concatenation TTS system.

  • [Flite (http://www.speech.cs.cmu.edu/flite/)] (Festival-lite) is a smaller, faster alterative version of Festival designed for embedded systems and high volume servers.

  • [MBROLA (http://tcts.fpms.ac.be/synthesis/mbrola)] is a freely available diphone concatenation system (back end).

Some very natural sounding commercial concatenative TTS systems with online demos: All of these have US English, most have other languages available.

[ASY (http://www.haskins.yale.edu/Haskins/MISC/ASY/asy)] is an articulatory synthesis program developed at Haskins Laboratories[?].

The Klatt Synthesizer[?], developed in 1980 by Dennis Klatt[?], is a cascade/parallel formant synthesizer whose basic approach still serves as the waveform synthesizer of many formant synthesis systems.

Well known external hardware devices:

Speech synthesis markup languages

A number of markup languages for rendition of text as speech in an XML compliant format, have been established, most recently the SSML[?] proposed by the W3C (still 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.

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.

External links

  • [Samples (http://www.tmaa.com/tts/comparison_USEng_highres.htm)] of commercial TTS systems.

See also speech processing, speech recognition

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