Abstract: Systems are configured for generating text-to-speech data in a personalized voice by training a neural text-to-speech machine learning model on natural speech data collected from a particular user, validating the identity of the user from which data is collected, and authorizing requests from users to use the personalized voice in generating new speech data. The systems are further configured to train a machine learning model as a neural text-to-speech model with generated personalized speech data.

Abstract: A text-to-speech (TTS) engine combines recorded speech with synthesized speech from a TTS synthesizer based on text input. The TTS engine receives the text input and identifies the domain for the speech (e.g. navigation, dialing, . . . ). The identified domain is used in selecting domain specific speech recordings (e.g. pre-recorded static phrases such as “turn left”, “turn right” . . . ) from the input text. The speech recordings are obtained based on the static phrases for the domain that are identified from the input text. The TTS engine blends the static phrases with the TTS output to smooth the acoustic trajectory of the input text. The prosody of the static phrases is used to create similar prosody in the TTS output.

Abstract: A text-to-speech (TTS) engine combines recorded speech with synthesized speech from a TTS synthesizer based on text input. The TTS engine receives the text input and identifies the domain for the speech (e.g. navigation, dialing, . . . ). The identified domain is used in selecting domain specific speech recordings (e.g. pre-recorded static phrases such as “turn left”, “turn right” . . . ) from the input text. The speech recordings are obtained based on the static phrases for the domain that are identified from the input text. The TTS engine blends the static phrases with the TTS output to smooth the acoustic trajectory of the input text. The prosody of the static phrases is used to create similar prosody in the TTS output.

Abstract: Techniques to create and share custom voice fonts are described. An apparatus may include a preprocessing component to receive voice audio data and a corresponding text script from a client and to process the voice audio data to produce prosody labels and a rich script. The apparatus may further include a verification component to automatically verify the voice audio data and the text script. The apparatus may further include a training component to train a custom voice font from the verified voice audio data and rich script and to generate custom voice font data usable by the TTS component. Other embodiments are described and claimed.

Abstract: Techniques to create and share custom voice fonts are described. An apparatus may include a preprocessing component to receive voice audio data and a corresponding text script from a client and to process the voice audio data to produce prosody labels and a rich script. The apparatus may further include a verification component to automatically verify the voice audio data and the text script. The apparatus may further include a training component to train a custom voice font from the verified voice audio data and rich script and to generate custom voice font data usable by the TTS component. Other embodiments are described and claimed.