Norsk i den digitale tidsalderen - Meta-Net

Speech Output Speech Synthesis
Speech Input
Signal Processing
user interface. For static utterances where the wording
does not depend on particular contexts of use or per-
sonal user data, this can deliver a rich user experience.
But more dynamic content in an utterance may suffer
from unnatural intonation because different parts of au-
dio files have simply been strung together. rough opti-
misation, today’s TTS systems are getting better at pro-
ducing natural-sounding dynamic utterances.
Interfaces in speech interaction have been considerably
standardised during the last decade in terms of their
various technological components. ere has also been
strong market consolidation in speech recognition and
speech synthesis. e national markets in the G20 coun-
tries (economically resilient countries with high popu-
lations) have been dominated by just five global play-
ers, with Nuance (USA) and Loquendo (Italy) being the
most prominent players in Europe. In 2011, Nuance an-
nounced the acquisition of Loquendo, which represents
a further step in market consolidation.
In the Norwegian TTS market, thirteen Norwegian
voices of varying quality are available, some of which
have been developed by the above mentioned European
players. ree voices have been developed by the Nor-
wegian company Lingit, which targets users groups with
reading and writing impairments. Another voice was de-
veloped by the Norwegian Library of Talking Books and
Braille in cooperation with their sister library in Sweden.
ere is also an active research community at the Nor-
Phonetic Lookup &
Intonation Planning
Recognition
5: Speech-based dialogue system
Natural Language
Understanding &
Dialogue
wegian University of Science and Technology in Trond-
heim.
Language resources for speech synthesis are
abundant for English but only to a lesser extent
for Norwegian.
e quality of speech synthesis depends heavily on avail-
able resources (in particular text corpora tagged for part
of speech, tokenisers and pronunciation lexicons) and
language specific research on for instance prosodic fea-
tures for the language in question. Such resources are
abundant for English but only to a lesser extent for Nor-
wegian, even if Norwegian is especially challenging due
to the wide variety of possible spelling variants and the
range of dialects; moreover the tonal accents in most
Norwegian dialects and the lack of a one-to-one relation
between sounds and letters pose challenges.
Regarding dialogue management technology and
know-how, the market is rather dominated by national,
smaller enterprises. MediaLT has developed a general
speech recognition engine used for dialogue manage-
ment for the visually impaired. Regarding speech-to-
text, Max Manus has integrated and localised Philips’
SpeechMagic for Norwegian hospitals. is system is
relatively successful, but it is limited to a relatively closed
domain (with a closed vocabulary). Recently Dragon
Dictation, a voice recognition application for mobile
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