This part of the book discusses the techniques required to actually
create the speech waveform from a complete phonetic and prosodic
description.
Traditionally we can identify three different methods that are
used for creating waveforms from such descriptions.
Articulatory synthesis is a method where the human vocal tract,
tongue, lips, etc are modeled in a computer. This method can produce
very natural sounding samples of speech but at present it requires too
much computational power to be used in pratical systems.
Formant synthesis is a technique where the speech
signal is broken down into iverlapping parts, such as formats (hence
the name), voicing, asperation, nasality etc. The output of individual
generators are then composed from streams of parameters describing the
each sub-part of the speech. With carefully tuned parameters the
quality of the speech can be close to that of natural speech, showing
that this encoiding is sufficient to represent human speech. However
automatic prediction of these parameters is harder and the typical
quality of synthesizers based on this technology can be very
understandable but still have a distinct no-human or robotic
quality. Format synthesizers are best typified MITalk, the work of
Dennis Klatt, and what was later commercialised as DECTalk. DECTalk is
at present probably the most familar form of speech synthesis to the
world. Dr Stephen Hawkins, Lucindian Professor of Mathematics at
Cambridge University, has lost the use of his voice and uses a formant
synthesizer for his speech.
Concatenative synthesis is the third form
which we will spend the most time on. Here waveforms are created by
concatenating parts of natural speech record from humans.
In the search for more natural synthesis there has been a move to use
recorded human speech rather than techniques to construct it from its
fundamental parts. The potential for using recorded speech has in some
sense been made possible by the increase in power of computer and
storage media. Format synthesizers require a relatively small amount of
data but concatenative speech synthesizer typically require much more disk
space to contain the inventory of speech sounds. And more recently
the size databases used has grown further as there is some relationhip
between voice quality and database size.
The techniques described here and the following chapters are concerned
solely of concatenative synthesis. Concatenative synthesis techniques
not only give the most natural sounding speech synthesis, it also is
the most accessible to the general user in that it is quite easy for us
to record speech and the technique used here to analyse it are, to the
most part, automatic.
The area of concatenative systems can be viewed as a complex continunium,
as there are many choices in selecting unit type, size etc. We have
included examples and techniques from the most conservative (i.e. most
likely to work) to the forefront of the art of voice building. It is
important to understand the space of possible synthesis techniques in
order to select the best one for your particular application. The
resources required to develop each of the basic types of concatenative
synthesizers varies greatly. It is possible to get a general speech
synthesizer working in English in under an hour, though its quality
isn't very good (though can be understandable). At the other extreme,
in the area of speech synthesis we have yet to develop a system that is
both natural and flexible to satisfy all synthesis requirements so the
task of building a voice may take a lifetime. However the following
chapter outline techniques which can be completed by an interest person
in as little a day or at most a week which can produce high, natural
sounding voices suitable for a wide range of computer speech
applications.
In order for synthesis of any piece of text we need to have examples of
every unit in the language to be synthesized. At some extreme this
would mean you'd need recordings of every sentence (or even paragraph)
of everything that needs to be said. This of course is impractical and
defeats the whole purpose of a having a synthesizer. Thus we need to
make some simplifying assumptions. The simplest (and most extreme)
is to assume that speech is made of up strings of discrete phonemes.
US English has (by one definiton) 43 different phonemes. That is
the fundamental sounds in the language, thus the word, "bit" is
made up of three phones /B/ /IH/ and /T/. The word "beat" however
is made up of the phonemes /B/ /IY/ and /T/.
In the following chapter we consider the absolute simplest waveform
synthesizer that consists of recording each phone int he language and
the resequencing them to form new words. Although this is a easy and
quick synthesizer to build it is immediately obvious that it is not of
very good quality. The reason being thate human speech just doesn't
consist of isloated phonemes concatenated to gether but that there are
articulatory effects that cross over multiple phones
(co-articulation). Thus the more practical technique is to build a
diphone synthesizer where we record each phoneme
in the context of each other phoneme.
However speech is more varied than that although we can modify the
selected diphones to obtain the desired prosody, such modification is
not as good as if it were actual spoken by a human. Thus the area of
general unit selection synthesis has grown where the datbases we
select from has many more examples of speech, in more contexts and not
just one example of each phoneme-phoneme transition in the language.
The size and design of a databases most suitable for unit selection is
difficult and we discuss this in the following chapter. The techniques
required to find the most approrpiate unit, taking into account,
phonetic context, word position, phrase position as well prosodic
context is important but finding the right palance of these features
is still something of an art. In Chapter 12 we present
a number techniques and experiments to build such synthesizers.
Although unit selection synthesizer clear offer the best quality
synthesis, their databases are substantial piece of work. They must be
preoperly labeled and although we include automatic alignment
techniques there will always be mistakes and hand correction is
certainly both desirable and worthwhile. But that takes time and
certain skills to do. When the unit selection is bad due to bad labels
inappropriate weight of features or just simply not enough good examples
int he dtaabase to choose from the quality can be serverely worse than
diphones so the work in tuning a unit selection synthesizer is as much
avoiding the bad examples as improving the good ones. The third chapter
on waveform syntehsizers offers a very rpactical compramise between
diphone (safe) synthesizers and unit selection (exciting) synthesizers.
In Chapter 5 on limited domain synthesis, we discuss how to target your recorded
database to a particular application and get the benefits of the high
quality of unit selection synthesis without the requirement of very
carefully labeled databases. In many case this thrid option is the
most practical.
Of course it is possible to combine approaches. This requries more care
in the design but sometimes offers the best of all techniques. Having a
targeted limited domain synthesizer which can cover most of the desired
language will be good but falling back on good unit selection
synthesizer for unknown words may be a real posibility.
Key choices:
size, type, prosodic modification, number of occurrences
Key positions in the space
uniphones, diphones
unit selection, limited domain vs open
Need diagram for space of synthesizers
