For an individual with a speech impairment, it can be necessary for them to use a device to produce synthesized speech to assist their communication. To fully support all functions of human speech communication: communication of information, maintenance of social relationships and displaying identity, the voice must be intelligible and natural-sounding. Ideally, it must also be capable of conveying the speaker’s vocal identity. A new approach based on Hidden Markov models (HMMs) has been proposed as a way of capturing sufficient information about an individual’s speech to enable a personalized speech synthesizer to be developed. This approach adapts a statistical model of speech towards the vocal characteristics of an individual. This chapter describes this approach and how it can be implemented using the HTS toolkit. Results are reported from a study that built personalized synthetic voices for two individuals with dysarthria. An evaluation of the voices by the participants themselves suggests that this technique shows promise for building personalized voices for individuals with progressive dysarthria even when their speech has begun to deteriorate.
Detecting Selective Sweeps: A New Approach Based on Hidden Markov Models