| RATIONALE: AWB: probably way too biased as a history |
The idea
that a machine could generate speech has been with us for some time,
but the realization of such machines has only really been practical
within the last 50 years. Even more recently, it's in the last 20
years or so that we've seen practical examples of text-to-speech
systems that can say any text they're given -- though it might be "wrong."
The creation of synthetic speech covers a whole range of processes,
and though often they are all lumped under the general term
text-to-speech, a good deal of work has gone
into generating speech from sequences of speech sounds; this would be
a speech-sound (phoneme) to audio waveform synthesis, rather than
going all the way from text to phonemes (speech sounds), and then to
sound.
One of the first practical application of speech synthesis was in 1936
when the U.K. Telephone Company introduced a speaking clock. It used
optical storage for the phrases, words, and part-words ("noun,"
"verb," and so on) which were appropriately concatenated to form
complete sentences.
Also around that time, Homer Dudley developed a mechanical device at
Bell Laboratories that operated through the movement of pedals, and mechanical
keys, like an organ. With a trained operator, it could be made to
create sounds that, if given a good set-up, almost sounded like
speech. Called the Voder, it was demonstrated
at the 1939 World's Fair in New York and San Francisco. A recording
of this device exists, and can be heard as part of a collection of
historical synthesis examples that were distributed on a record as
part of [klatt87].
The realization that the speech signal could be decomposed as a
source-and-filter model, with the glottis acting as a sound source and
the oral tract being a filter, was used to build analog electronic
devices that could be used to mimic human speech. The
vocoder, also developed by Homer Dudley, is one
such example. Much of the work in synthesis in the 40s and 50s was
primarily concerned with constructing replicas of the signal itself
rather than generating the phones from an abstract form like text.
Further decomposition of the speech signal allowed the development of
formant synthesis, where collections of signals
were composed to form recognization speech. The prediction of
parameters that compactly represent the signal, without the loss of
any information critical for reconstruction, has always been, and
still is, difficult. Early versions of formant synthesis allowed
these to be specified by hand, with automatic modeling as a goal.
Today, formant synthesizers can produce high quality, recognizable
speech if the parameters are properly adjusted, and these systems can
work very well for some applications. It's still hard to get
fully natural sounding speech from these when the process is fully
automatic -- as it is from all synthesis methods.
With the rise of digital representations of speech, digital signal
processing, and the proliferation of cheap, general-purpose computer
hardware, more work was done in concatenation of natural recorded
speech. Diphones appeared; that is, two
adjacent half-phones (context-dependent phoneme realizations), cut in
the middle, joined into one unit. The justification was that phone
boundaries are much more dynamic than stable, interior parts of
phones, and therefore mid-phone is a better place to concatenate
units, as the stable points have, by definition, little rapid change,
whereas there are rapid changes at the boundaries that depend upon the
previous or next unit.
The rise of concatenative synthesis began in the 70s, and has largely
become practical as large-scale electronic storage has become cheap
and robust. When a megabyte of memory was a significant part of
researchers salary, less resource-intensive techniques were worth
their... weight in saved cycles in gold, to use an odd metaphor. Of
course formant, synthesis can still require significant computational
power, even if it requires less storage; the 80s speech synthesis
relied on specialized hardware to deal with the constraints of the
time.
In 1972, the standard Unix manual (3rd edition) included commands to
process text to speech, form text analysis, prosodic prediction,
phoneme generation, and waveform synthesis through a specialized piece
of hardware. Of course Unix had only about 16 installations at the
time and most, perhaps even all, were located in Bell Labs at Murray
Hill.
Techniques were developed to compress (code) speech in a way that it
could be more easily used in applications. The Texas Instruments
Speak 'n Spell toy, released in the late 70s,
was one of the early examples of mass production of speech synthesis.
The quality was poor, by modern standards, but for the time it was
very impressive. Speech was basically encoded using LPC (linear
Predictive Coding) and mostly used isolated words and letters though
there were also a few phrases formed by concatenation. Simple
text-to-speech (TTS) engines based on specialised chips became popular
on home computers such as the BBC Micro in the UK and the Apple ][.
Dennis Klatt's MITalk synthesizer [allen87] in many
senses defined the perception of automatic speech synthesis to the
world at large. Later developed into the product DECTalk, it produces
somewhat robotic, but very understandable, speech. It is a formant
synthesizer, reflecting the state of the art at the time.
Before 1980, research in speech synthesis was limited to the large
laboratories that could afford to invest the time and money for
hardware. By the mid-80s, more labs and universities started to join
in as the cost of the hardware dropped. By the late eighties, purely
software synthesizers became feasible; the speech quality was still
decidedly inhuman (and largely still is), but it could be generated in
near real-time.
Of course, with faster machines and large disk space, people began to
look to improving synthesis by using larger, and more varied
inventories for concatenative speech. Yoshinori Sagisaka at Advanced
Telecommunications Research (ATR) in Japan developed nuu-talk
[nuutalk92] in the late 80s and early 90s. It
introduced a much larger inventory of concatenative units; thus,
instead of one example of each diphone unit, there could be many, and
an automatic, acoustically based distance function was used to find
the best selection of sub-word units from a fairly broad database of
general speech. This work was done in Japanese, which has a much
simpler phonetic structure than English, making it possible to get
high quality with a relatively small databases. Even up through 1994,
the time needed to generate of the parameter files for a new voice in
nuu-talk (503 senetences) was on the order of several days of CPU
time, and synthesis was not generally possible in real time.
With the demonstration of general unit selection
synthesis in English in Rob Donovan's PhD work
[donovan95], and ATR's CHATR system
([campbell96] and [hunt96]), by
the end of the 90's, unit selection had become a hot topic in speech
synthesis research. However, despite examples of it working
excellently, generalized unit selection is known for producing very
bad quality synthesis from time to time. As the optimial search and
selection agorithms used are not 100% reliable, both high and low
quality synthesis is produced -- and many diffilculties still exists
in turning general corpora into high-quality synthesizers as of this
writing.
Of course, the development of speech synthesis is not isolated from
other developments in speech technology. Speech recognition, which
has also benefited from the reduction in cost of computational power
and increased availability of general computing into the populace,
informs a the work on speech synthesis, and vice versa. There are now
many more people who have the computational resouces and interest in
running speech applications, and this ability to run such applications
puts the demand on the technology to deliver both working recognition
and acceptable quality speech synthesis.
The availability of free and semi-free synthesis systems, such as
the Festival Speech Synthesis System and the MBROLA project, makes the
cost of entering the field of speech synthesis much lower, and many
more groups have now joined in the development.
However, although we are now at the stage were talking computers are
with us, there is still a great deal of work to be done. We can now
build synthesizers of (probably) any language that can produce
reconizable speech, with a sufficient amount of work; but if we are to
use speech to receive information as easily when we're talking with
computers as we do in everyday conversation, synthesized speech must
be natural, controllable and efficient (both in rendering and in the
building of new voices).
