SALB: Speech synthesis of Auditory Lecture books for Blind children
In this project we want to evaluate HMM-based synthesis of different language varieties (standard, dialect, sociolect) for auditive lecture books. Moreover, we want to analyze the influence of different social roles (teacher vs student) as well as of self-perception and perception of others, that exists between the listener and the person whose voice is synthesized.
Information technology in general and speech technology in particular have enhanced the accessibility to information for blind and partially sighted users. Nowadays, blind users are able to access the entire amount of information on the web by using speech-based User Interfaces (UI), the advantage of those over Braille-lines being clearly increased cost-efficiency and the usage without any special training. Through a combination of speech-based UIs and Braille-lines a more robust interaction is possible. Parametric methods of speech synthesis are nowadays used in many speech-based UIs, since they use up little memory, can be calculated efficiently and are highly flexible. Regarding intelligibility of speech these methods are sufficient, but there is still need for improvements in the quality and intelligibility of speech at high speaking rates.
Parametric methods that are based on hidden Markov models (HMM), allow a high degree of flexibility. Through model adaptation, it is easily possible to create voices for certain speakers. Adaptive methods can also be used for the generation of fast speech, which is very important for blind users to interact efficiently with an information system.
For further details, please visit the SALB project webpage.
BMWF - Sparkling Science http://www.sparklingscience.at
Position EPSRC Career Acceleration Fellow Office location IF 3.06 Telephone number +44 131 651 5637 Email address Personal homepage http://homepages.inf.ed.ac.uk/jyamagis/ Research interests Speech synthesis and recognition Publications list available here Research profile available here
BMWF - Sparkling Science