In our last post, we discussed speech-to-text
technology, which has a background that varies from the history and current
applications of text-to-speech technology. Text-to-speech, through the process
of speech synthesis, has been in the works for a much longer time than
speech-to-text, and it is more concerned with providing technology to aid
people as opposed to the purpose of inputting information. However,
text-to-speech can be successfully paired with speech-to-text to provide a type
of human-computer interaction that does not require a keyboard.
The earliest known evidence of speech synthesis dates back
to the Eighteenth Century, when Christian Kratzenstein explained physiological
differences between five long vowels (/a/, /e/, /i/, /o/, and /u/) and made
apparatus to produce them artificially in 1779 St. Petersburg. He
constructed acoustic resonators that bore similarities to the human vocal
tract, and when activated with vibrating reeds like in musical instruments,
they produced sounds similar to the vowels that they were designed to sound
like. Not too soon later, in 1791 in Vienna, Wolfgang von Kempelen introduced
his Acoustic-Mechanical Speech Machine, which could produce single sounds,
along with some sound combinations. In the mid-1800s, Charles Wheatstone produced
a more advanced and famous version of Kempelen’s machine, which was able to
produce vowels and most of the consonants, as well as some words. Vowels in
this machine were produced with a vibrating reed, and consonants were produced
with turbulent flow through a suitable passage with reed-off.
The technology was present in the early Twentieth Century to
create electronic resonators, but it was not until the 1930s when the first
electric speech synthesizers were created. Homer Dudley at Bell Laboratories
accomplished this, initially with the “Voder”, which was
first presented at the 1939 and 1940 World’s Fairs. The Voder functioned by
having an array of ten parallel resonators that spanned all frequencies of the
speech spectrum, and it was operated through the use of a keyboard that
controlled different aspects of sound through keys and a pedal. This system was
very complicated, and even after months of training, operators were only able to
produce intelligible language if it were paired with the context of a question.
Dudley’s other device, the channel vocoder, was released around the same time
and it has lasted as the basis for many
speech synthesis devices that are still in use today. This machine was divided
into one-half that was meant to analyze an incoming speech signal from natural
sound parameters, and another half that used that signal to produce a synthetic
sound.
In recent history, speech synthesis has shifted from these
large vocoder machines and has now become computer-based, making use of several
different forms, the greatest-used of which is concatenative data-driven sound
synthesis. Methods using this access a large database of source sounds,
segmented into heterogeneous units, and a unit
selection algorithm that finds the units that match best the sound to be
synthesized, or the target. This procedure has been responsible for modern
synthetic speech sounding much more like that spoken by a human being, as
opposed to the more-robotic sounding voices of the past. This has mostly
displaced a process known as diphone
synthesis, in which recorded speech was split into phonemes, and all
diphones, or two adjacent half-phones, were modeled. Since the “center” of a
phonetic realization is the most stable region, all possible phonemes can be
created in a language by modeling the diphones, but it works better for
languages that have a smaller variety of diphones.
In our speech-to-text
post, we looked at the differences between how computers and brains process
speech. Generating speech is different from processing speech in the human
body, since it requires the lungs,
voice box, and nasal and oral cavities to produce meaningful audible
utterances that can project to a listener, while the listener who is processing
the speech uses a variety of parts
of the inner ear to recognize sound waves. However, generating speech is
similar to processing heard speech because it is performed by a small part of
the brain. Broca’s
area, also located on the left hemisphere of the brain like Wernicke’s area,
but on the lower portion of the frontal lobe, is involved with speech
production, facial neuron control, and, since it is connected to Wernicke’s
area, language processing. In addition, like Wernicke’s area, damage to Broca’s
area causes Broca’s
Aphasia, a condition in which people can have difficulty producing
grammatical sentences with a complex language structure, both orally and in
writing. Paul Broca discovered this in 1861, while treating a patient who could
only say the word “tan”.
Broca’s area makes use of the different phonemes that we
have learned to guide our language production. This, like the unit selection
algorithm used in speech synthesis technology, determines the sounds that we
use to form the morphemes (smallest meaningful units) in a statement that we
speak. However, methods such as concatenative sound synthesis and diphone
selection only indirectly create intelligible words, since they are making use
of prerecorded sounds spoken by an actual human being. Articulary speech
synthesis is an alternative to these methods that directly simulates the
principles of speech production by using a mechanism created to emulate
the movement of the vocal tract. This simulates the different physical
sound sources that are responsible for speech produced by the human body.
Synthetic speech currently has uses in assisting
communication for several
different purposes, including reading aides for the blind and speaking
aides for the deaf. This technology also has applications in education, in
which the artificially produced speech is programmed for specific tasks that
help to teach spelling and pronunciation in different languages. Aside from
general assistance, speech synthesis also has been put to use in the
telecommunications industry, for things such as telephone inquiry systems.
One of the more recent uses of speech synthesis has been the
pairing of it with speech-to-text speech recognition to create a type of
human-computer interaction that is based entirely off speaking. The most widely
used examples of this are virtual assistants Siri,
present in Apple products, and Cortana,
who is found in Microsoft products. Both of these use concatenative synthesis
to form the computer-human interaction, each taking in many recordings of an
individual person’s voice so that the software can form intelligible language.
As this software improves and people come to be more reliant on interacting
with their phones and computers by speaking, it will be interesting to see
which voices are chosen to interact with users. Siri, while having different
voices in different countries and now a male version, is voiced in American
English by Susan
Bennett, who is also the voice of Delta Airlines. The voice of Cortana, Jen
Taylor, was not necessarily chosen because she has a neutral voice that would
be appropriate to talk to many different people, but because she is the same actor
who voiced
the character Cortana in the Halo video game series from which the
assistant derives her name.
With the unit selection of concatenative synthesis only requiring
one voice to create a speech synthesis interface, there is no need to have
different voice actors for the same language. However, Apple has different
variations of English for Siri depending on the part of the world in which it
is spoken, something that can be changed in an
IPhone’s Settings. It is clear from this that these companies recognize
that there is a clear difference between the variations of a language that is
spoken in different countries, and that people might want to hear a voice that
is similar to their own.
However, a voice that is used will always have some
regional variation, never being exactly representative of an entire nation.
Even though the solution to this would be to include different regional
dialects, this could problematically lead to marginalization of different ways
of speaking that are considered to be greatly distanced from the standard of
that country by having the actors perform them in a comical way. For example,
while performing a New York accent, even if it was the actor’s place of origin,
he or she might make particular phonetic choices that would end up sounding
cartoonish. The ultimate resolution to all of this would be software that
includes every phoneme present throughout the world, being able to make use of
the ones present in the region in which it is being used. This type of software
will not be likely for some time, so until then we will have to make due by
interacting with voices that we can understand but do not sound exactly like
us.
Standards
relating to speech and software
ergonomics using voice input may be found on the ANSI Webstore.
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