Andrew P. Breen

Towards automatic phonetic segmentation for TTS

Phonetic segmentation is an important step in the development of a concatenative TTS voice. This paper introduces a segmentation process consisting of two phases. First, forced alignment is performed using an HMM-GMM model. The resulting segmentation is then locally refined using an SVM based boundary model. Both the models are derived from multi-speaker data using a speaker adaptive training procedure. Evaluation results are obtained on the TIMIT corpus and on a proprietary single-speaker TTS corpus.

Creating Expressive TTS Voices for Conversation Agent Applications

Text-to-Speech has traditionally been viewed as a “black box” component, where standard “portfolio” voices are typically offered with a professional but “neutral” speaking style. For commercially important languages many different portfolio voices may be offered all with similar speaking styles. A customer wishing to use TTS will typically choose one of these voices. The only alternative is to opt for a “custom voice” solution. In this case, a customer pays for a TTS voice to be created using their preferred voice talent. Such an approach allows for some “tuning” of the scripts used to create the voice. Limited script elements may be added to provide better coverage of the customer’s expected domain and “gilded phrases” can be included to ensure that specific phrase fragments are spoken perfectly. However, even with such an approach the recording style is strictly controlled and standard scripts are augmented rather than redesigned from scratch. The “black box” approach to TTS allows for systems to be produced which satisfy the needs of a large number of customers, even if this means that solutions may be limited in the persona they present.