book2

Best algorithm for LZW ..C language

The primary analysis technique used for this purpose is
.ul
linear prediction
of speech, and this is treated in some detail in Chapter 6.  It also reduces drastically the
data-rate of speech, by a factor of around 50.
It is likely that many voice-response systems in the short- and medium-term
future will use linear predictive representations for utterance storage.
.pp
For maximum flexibility, however, it is preferable to store a textual
representation of the utterance.
There is an important distinction between speech
.ul
storage,
where an actual human utterance is recorded, perhaps processed to lower
the data-rate, and stored for subsequent regeneration when required,
and speech
.ul
synthesis,
where the machine produces its own individual utterances which are not based
on recordings of a person saying the same thing.  The difference is summarized
in Figure 1.5.
.FC "Figure 1.5"
In both cases something is stored:  for the first it is
a direct representation of an actual human utterance, while for the second
it is a typed
.ul
description
of the utterance in terms of the sounds, or phonemes, which constitute it.
The accent and tone of voice of the human speaker will be apparent in
the stored speech output, while for synthetic speech the accent is the
machine's and the tone of voice is determined by the synthesis program.
.pp
Probably the most attractive representation of utterances in man-machine
systems is ordinary English text, as used by the Kurzweil reading machine.
But, as noted above, this poses extraordinarily difficult problems for the
synthesis procedure, and these inevitably result in severely degraded speech.
Although in the very long term these problems may indeed be solved,
most speech output systems can adopt as their representation of an utterance
a description of it which explicitly conveys the difficult features of
intonation, rhythm, and even pronunciation.
In the kind of applications described above (barring the reading machine),
input will be prepared by a
programmer as he builds the software system which supports the interactive
dialogue.
Although it is important that the method of specifying utterances be easily
learned, it is not necessary that plain English
is used.  It should be simple for the programmer to enter new
utterances and modify them on-line in cut-and-try attempts to render the
man-machine dialogue as natural as possible.  A phonetic input
can be quite adequate for this, especially if the system allows the
programmer to hear immediately the synthesized version of the message
he types.  Furthermore, markers which indicate rhythm and intonation can
be added to the message so that the system does not have to deduce these features
by attempting to "understand" the plain text.
.pp
This brings us to another disadvantage of speech storage as compared with
speech synthesis.  To provide utterances for a voice response system using
stored human speech, one must assemble together special input hardware,
a quiet room, and (probably) a dedicated computer.  If the speech is to be
heavily encoded, either expensive special hardware is required or the encoding
process, if performed by software on a general-purpose computer, will take
a considerable length of time (perhaps hundreds of times real-time).  In
either case, time-consuming editing of the speech will be necessary, with
follow-up recordings to clarify sections of speech which turn out to be
unsuitable or badly recorded.  If at a later date the voice response
system needs modification, it will be necessary to recall the same speaker,
or re-record the entire utterance set.  This discourages the application
programmer from adjusting his dialogue in the light of experience.
Synthesizing from a textual representation, on the other hand, allows him
to change a speech prompt as simply as he could a VDU one, and evaluate
its effect immediately.
.pp
We will return to methods of digitizing and compacting speech in Chapters 3
and 4, and carry on to consider speech synthesis in subsequent chapters.
Firstly, however, it is necessary to take a look at what speech is and how
people produce it.
.sh "1.8  References"
.LB "nnnn"
.[
$LIST$
.]
.LE "nnnn"
.sh "1.9  Further reading"
.pp
There are remarkably few general books on speech output, although a
substantial specialist literature exists for the subject.
In addition to the references listed above, I suggest that you look
at the following.
.LB "nn"
.\"Ainsworth-1976-1
.]-
.ds [A Ainsworth, W.A.
.ds [D 1976
.ds [T Mechanisms of speech recognition
.ds [I Pergamon
.nr [T 0
.nr [A 1
.nr [O 0
.][ 2 book
.in+2n
A nice, easy-going introduction to speech recognition, this book covers
the acoustic structure of the speech signal in a way which makes
it useful as background reading for speech synthesis as well.
It complements Lea, 1980, cited above; which presents more recent results
in greater depth.
.in-2n
.\"Flanagan-1973-2
.]-
.ds [A Flanagan, J.L.
.as [A " and Rabiner, L.R. (Editors)
.ds [D 1973
.ds [T Speech synthesis
.ds [I Wiley
.nr [T 0
.nr [A 0
.nr [O 0
.][ 2 book
.in+2n
This is a collection of previously-published research papers on speech
synthesis, rather than a unified book.
It contains many of the classic papers on the subject from 1940\ -\ 1972,
and is a very useful reference work.
.in-2n
.\"LeBoss-1980-3
.]-
.ds [A LeBoss, B.
.ds [D 1980
.ds [K *
.ds [T Speech I/O is making itself heard
.ds [J Electronics
.ds [O May\ 22
.ds [P 95-105
.nr [P 1
.nr [T 0
.nr [A 1
.nr [O 0
.][ 1 journal-article
.in+2n
The magazine
.ul
Electronics
is an excellent source of up-to-the-minute news, product announcements,
titbits, and rumours in the commercial speech technology world.
This particular article discusses the projected size of the voice
output market and gives a brief synopsis of the activities of several
interested companies.
.in-2n
.\"Witten-1980-5
.]-
.ds [A Witten, I.H.
.ds [D 1980
.ds [T Communicating with microcomputers
.ds [I Academic Press
.ds [C London
.nr [T 0
.nr [A 1
.nr [O 0
.][ 2 book
.in+2n
A recent book on microcomputer technology, this is unusual in that
it contains a major section on speech communication
with computers (as well as ones
on computer buses, interfaces, and graphics).
.in-2n
.LE "nn"
.EQ
delim $$
.EN
.CH "2  WHAT IS SPEECH?"
.ds RT "What is speech?
.ds CX "Principles of computer speech
.pp
People speak by using their vocal cords as a sound source, and making rapid
gestures of the articulatory organs (tongue, lips, jaw, and so on).
The resulting changes in shape of the vocal tract allow production
of the different sounds that we know as the vowels and consonants of
ordinary language.
.pp
What is it necessary to learn about this process for the purposes of
speech output from computers?
That depends crucially upon how speech is represented in the system.
If utterances are stored as time waveforms \(em and this is what we will be
discussing in the next chapter \(em the structure of speech is not important.
If frequency-related parameters of particular natural utterances are
stored, then it is advantageous to take into account some of the
acoustic properties of the speech waveform.
.pp
This point can be brought into focus by contrasting the transmission
(or storage) of speech with that of real-life television pictures,
as has been proposed for a videophone service.
Massive data reductions, of the order of 50:1, can be achieved for speech,
using techniques that are described in later chapters.  For pictures,
data reduction is still an important issue \(em even more so for the
videophone than for the telephone, because of the vastly higher
information rates involved.
Unfortunately, the potential for data reduction is much
smaller \(em nothing like the 50:1 figure quoted above.
This is because speech sounds have definite characteristics, imparted
by the fact that they are produced by a human vocal tract, which
can be exploited for data reduction.
Television pictures have no equivalent generative structure, for
they show just those things that the camera points at.
.pp
Moving up from frequency-related parameters of
.ul
particular
utterances, it
is possible to store such parameters in a
.ul
general
form which characterizes the sound segments that appear in spoken language.
This immediately raises the issue of
.ul
classification
of sound segments, to form a basis for storing generalized acoustic
information and for retrieval of the information needed to synthesize
any particular utterance.
Speech is by nature continuous, and any synthesis system based upon
discrete classification must come to terms with this by tackling
the problems of transition from one segment to another,
and local modification of sound segments as a function of their context.
.pp
This brings us to another level of representation.
So far we have talked of the
.ul
acoustic
nature of speech, but when we have to cope with transitions between
discrete sound segments it may be fruitful to consider
.ul
articulatory
properties as well.
Any model of the speech production process
is in effect a model of the articulatory process that generates the speech.
Some speech research is concerned with
modelling
the vocal tract directly, rather than modelling the acoustic output from it.
One might specify, for example, position of tongue and posture of jaw and lips
for a vowel, instead of giving frequency-related
characteristics of it.  This is a potent
tool in linguistic research, for it brings one closer to human production of
speech \(em in particular to the connection between brain and articulators.
.pp
Articulatory
synthesis holds a promise of high-quality speech, for the transitional
effects caused by tongue and jaw inertia can be modelled directly.
However, this potential has
not yet been realized.
Speech from current articulatory models is of much poorer quality than
that from acoustically-based synthesis methods.
The major problem is in gaining data about articulatory
behaviour during running speech \(em it is much easier to perform acoustic
analysis on the resulting sound than it is to examine the vocal organs in
action.  Because of this, the subject is not treated in this book.
We will only look at articulatory properties insofar as they help us
to understand, in a qualitative way, the acoustic nature of speech.
.pp
Speech, however, is much more than mere articulation.
Consider \(em admittedly a rather extreme and chauvinistic example \(em the
number of ways a girl can say "yes".
Breathy voice, slow tempo, low pitch \(em these are all characteristics which
affect the utterance as a whole, rather than being classifiable into
individual sound segments.  Linguists call them "prosodic" or
"suprasegmental" features, for they relate to overall aspects of the
utterance, and distinguish them from "segmental" ones which concern
the articulation of individual segments of syllables.
The most important prosodic features are pitch, or fundamental frequency
of the voice, and rhythm.
.pp
This chapter provides a brief introduction to the nature of the speech
signal.  Depending upon what speech output techniques we use, it may be
necessary to understand something of the acoustic nature of the speech
signal; the system that generates it (the vocal tract); commonly-used
classifications of sound segments; and the prosodic aspects of speech.
This material is little used in the early chapters of the book, but
becomes increasingly important as the story unfolds.
Hence you may skip the remainder of this chapter if you wish, but
should return to it later to pick up more background whenever it
becomes necessary.
.sh "2.1  The anatomy of speech"
.pp
The so-called "voiced" sounds of speech \(em like the sound you make when
you say "aaah" \(em are produced by passing air up from the lungs through
the larynx or voicebox, which is situated just behind the Adam's apple.
The vocal tract from the larynx to the lips acts as a resonant cavity,
amplifying certain frequencies and attenuating others.
.pp
The waveform generated by the larynx, however, is not simply sinusoidal.
(If it were, the vocal tract resonances would merely
give a sine wave of the same frequency but amplified or
attenuated according to how close it was to the nearest resonance.)  The
larynx contains two folds of skin \(em the vocal cords \(em which blow apart and flap
together again in each cycle of the pitch period.
The pitch of a male voice in speech varies from as low as 50\ Hz
(cycles per second) to perhaps
250\ Hz, with a typical median value of 100\ Hz.
For a female voice the range is higher, up to about 500\ Hz in speech.
Singing can go much higher:  a top C sung by a soprano has a frequency
of just over 1000\ Hz, and some opera singers can reach
substantially higher than this.
.pp
The flapping action of the vocal cords
gives a waveform which can be approximated by a
triangular pulse (this and other approximations will be discussed in
Chapter 5).