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.
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)]
There are two main technologies used for the generating synthetic speech waveforms: concatenative synthesis and formant 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:
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.
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:
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."
Some freely available text-to-speech systems:
Some very natural sounding commercial concatenative TTS systems with online demos: All of these have US English, most have other languages available.
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:
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.