Abstract:
Methods and systems for adaptation of synthetic speech in an environment are described. In an example, a device, which may include a text-to-speech (TTS) module, may be configured to determine characteristics of an environment of the device. The device also may be configured to determine, based on the one or more characteristics of the environment, speech parameters that characterize a voice output of the text-to-speech module. Further, the device may be configured to process a text to obtain the voice output corresponding to the text based on the speech parameters to account for the one or more characteristics of the environment.
Abstract:
Methods, systems, and computer-readable media for text-to-speech synthesis using an autoencoder. In some implementations, data indicating a text for text-to-speech synthesis is obtained. Data indicating a linguistic unit of the text is provided as input to an encoder. The encoder is configured to output speech unit representations indicative of acoustic characteristics based on linguistic information. A speech unit representation that the encoder outputs is received. A speech unit is selected to represent the linguistic unit, the speech unit being selected from among a collection of speech units based on the speech unit representation output by the encoder. Audio data for a synthesized utterance of the text that includes the selected speech unit is provided.
Abstract:
A method and system is disclosed for building a speech database for a text-to-speech (TTS) synthesis system from multiple speakers recorded under diverse conditions. For a plurality of utterances of a reference speaker, a set of reference-speaker vectors may be extracted, and for each of a plurality of utterances of a colloquial speaker, a respective set of colloquial-speaker vectors may be extracted. A matching procedure, carried out under a transform that compensates for speaker differences, may be used to match each colloquial-speaker vector to a reference-speaker vector. The colloquial-speaker vector may be replaced with the matched reference-speaker vector. The matching-and-replacing can be carried out separately for each set of colloquial-speaker vectors. A conditioned set of speaker vectors can then be constructed by aggregating all the replaced speaker vectors. The condition set of speaker vectors can be used to train the TTS system.
Abstract:
A device may receive an input indicative of acoustic feature parameters associated with speech. The device may determine a modulated noise representation for noise pertaining to one or more of an aspirate or a fricative in the speech based on the acoustic feature parameters. The aspirate may be associated with a characteristic of an exhalation of at least a threshold amount of breath. The fricative may be associated with a characteristic of airflow between two or more vocal tract articulators. The device may also provide an audio signal indicative of a synthetic audio pronunciation of the speech based on the modulated noise representation.
Abstract:
A method and system is disclosed for non-parametric speech conversion. A text-to-speech (TTS) synthesis system may include hidden Markov model (HMM) HMM based speech modeling for both synthesizing output speech. A converted HMM may be initially set to a source HMM trained with a voice of a source speaker. A parametric representation of speech may be extract from speech of a target speaker to generate a set of target-speaker vectors. A matching procedure, carried out under a transform that compensates for speaker differences, may be used to match each HMM state of the source HMM to a target-speaker vector. The HMM states of the converted HMM may be replaced with the matched target-speaker vectors. Transforms may be applied to further adapt the converted HMM to the voice of target speaker. The converted HMM may be used to synthesize speech with voice characteristics of the target speaker.
Abstract:
A method and system for is disclosed for cross-lingual voice conversion. A speech-to-speech system may include hidden Markov model (HMM) HMM based speech modeling for both recognizing input speech and synthesizing output speech. A cross-lingual HMM may be initially set to an output HMM trained with a voice of an output speaker in an output language. An auxiliary HMM may be trained with a voice of an auxiliary speaker in an input language. A matching procedure, carried out under a transform that compensates for speaker differences, may be used to match each HMM state of the output HMM to a HMM state of the auxiliary HMM. The HMM states of the cross-lingual HMM may be replaced with the matched states. Transforms may be applied to adapt the cross-lingual HMM to the voices of the auxiliary speaker and of an input speaker. The cross-lingual HMM may be used for speech synthesis.
Abstract:
An input signal that includes linguistic content in a first language may be received by a computing device. The linguistic content may include text or speech. The computing device may associate the linguistic content in the first language with one or more phonemes from a second language. The computing device may also determine a phonemic representation of the linguistic content in the first language based on use of the one or more phonemes from the second language. The phonemic representation may be indicative of a pronunciation of the linguistic content in the first language according to speech sounds of the second language.
Abstract:
An input signal that includes linguistic content in a first language may be received by a computing device. The linguistic content may include text or speech. Based on an acoustic feature comparison between a plurality of first-language speech sounds and a plurality of second-language speech sounds, the computing device may associate the linguistic content in the first language with one or more phonemes from a second language. The computing device may also determine a phonemic representation of the linguistic content in the first language based on use of the one or more phonemes from the second language. The phonemic representation may be indicative of a pronunciation of the linguistic content in the first language according to speech sounds of the second language.
Abstract:
A device may identify a plurality of sources for outputs that the device is configured to provide. The plurality of sources may include at least one of a particular application in the device, an operating system of the device, a particular area within a display of the device, or a particular graphical user interface object. The device may also assign a set of distinct voices to respective sources of the plurality of sources. The device may also receive a request for speech output. The device may also select a particular source that is associated with the requested speech output. The device may also generate speech having particular voice characteristics of a particular voice assigned to the particular source.
Abstract:
A device may receive a speech signal. The device may determine acoustic feature parameters for the speech signal. The acoustic feature parameters may include phase data. The device may determine circular space representations for the phase data based on an alignment of the phase data with given axes of the circular space representations. The device may map the phase data to linguistic features based on the circular space representations. The linguistic features may be associated with linguistic content that includes phonemic content or text content. The device may provide a synthetic audio pronunciation of the linguistic content based on the mapping.