Location

Hilton Waikoloa Village, Hawaii

Event Website

http://hicss.hawaii.edu/

Start Date

1-3-2018

End Date

1-6-2018

Description

IBM has recently launched Project Intu, which extends the existing web-based cognitive service Watson with the Internet of Things to provide an intelligent personal assistant service. We propose a voice customization service that allows a user to directly customize the voice of Intu. The method for voice customization is based on IBM Watson’s text-to-speech service and voice conversion model. A user can train the voice conversion model by providing a minimum of approximately 100 speech samples in the preferred voice (target voice). The output voice of Intu (source voice) is then converted into the target voice. Furthermore, the user does not need to offer parallel data for the target voice since the transcriptions of the source speech and target speech are the same. We also suggest methods to maximize the efficiency of voice conversion and determine the proper amount of target speech based on several experiments. When we measured the elapsed time for each process, we observed that feature extraction accounts for 59.7% of voice conversion time, which implies that fixing inefficiencies in feature extraction should be prioritized. We used the mel-cepstral distortion between the target speech and reconstructed speech as an index for conversion accuracy and found that, when the number of target speech samples for training is less than 100, the general performance of the model degrades.

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Jan 3rd, 12:00 AM Jan 6th, 12:00 AM

Customization of IBM Intu’s Voice by Connecting Text-to-Speech Services and a Voice Conversion Network

Hilton Waikoloa Village, Hawaii

IBM has recently launched Project Intu, which extends the existing web-based cognitive service Watson with the Internet of Things to provide an intelligent personal assistant service. We propose a voice customization service that allows a user to directly customize the voice of Intu. The method for voice customization is based on IBM Watson’s text-to-speech service and voice conversion model. A user can train the voice conversion model by providing a minimum of approximately 100 speech samples in the preferred voice (target voice). The output voice of Intu (source voice) is then converted into the target voice. Furthermore, the user does not need to offer parallel data for the target voice since the transcriptions of the source speech and target speech are the same. We also suggest methods to maximize the efficiency of voice conversion and determine the proper amount of target speech based on several experiments. When we measured the elapsed time for each process, we observed that feature extraction accounts for 59.7% of voice conversion time, which implies that fixing inefficiencies in feature extraction should be prioritized. We used the mel-cepstral distortion between the target speech and reconstructed speech as an index for conversion accuracy and found that, when the number of target speech samples for training is less than 100, the general performance of the model degrades.

http://aisel.aisnet.org/hicss-51/da/business_intelligence_case_studies/3