Voice personality transformation

Savic, M.; Nam, Il-Hyun

doi:10.1016/1051-2004(91)90099-7

Cited by 21 publications

(8 citation statements)

References 2 publications

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“…In the past, one of the main methods of voice conversion was to map source speech onto target speech discretely [21,22]. In [21], codebooks for source voices, target voices, and mapping are constructed for voice conversion.…”

Section: Voice Conversionmentioning

confidence: 99%

See 1 more Smart Citation

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

Song¹,

Kim

Lee

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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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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Section: Voice Conversionmentioning

confidence: 99%

“…In [21], codebooks for source voices, target voices, and mapping are constructed for voice conversion. In [22], the index of the target voice's codebook is mapped from the feature vector of the source speech by using a feedforward neural network.…”

Section: Voice Conversionmentioning

confidence: 99%

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

Song¹,

Kim

Lee

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…Voice personality transformation [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] is a process by which voice personality is altered, so that one voice is made to sound like another. The process has numerous applications in a variety of areas such as personification of text-to-speech synthesis systems, preprocessing for speech recognition [17] , and enhancing the intelligibility of abnormal speech [8] .…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, feature parameters that represent the VTF including formant frequencies [4,5] , the linear prediction coefficient cepsturm (LPCC) [2,10,11] and LSP (Line Spectrum Pair) coefficients [9] , have been widely used in voice personality transformation. In the presented study, the LPCC was used as a feature parameter that represents the VTF.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection-based Voice Transformation

Lee¹

2012

The Journal of the Acoustical Society of Korea

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A voice transformation (VT) method that can make the utterance of a source speaker mimic that of a target speaker is described. Speaker individuality transformation is achieved by altering three feature parameters, which include the LPC cepstrum, pitch period and gain. The main objective of this study involves construction of an optimal sequence of features selected from a target speaker's database, to maximize both the correlation probabilities between the transformed and the source features and the likelihood of the transformed features with respect to the target model. A set of two-pass conversion rules is proposed, where the feature parameters are first selected from a database then the optimal sequence of the feature parameters is then constructed in the second pass. The conversion rules were developed using a statistical approach that employed a maximum likelihood criterion. In constructing an optimal sequence of the features, a hidden Markov model (HMM) was employed to find the most likely combination of the features with respect to the target speaker's model. The effectiveness of the proposed transformation method was evaluated using objective tests and informal listening tests. We confirmed that the proposed method leads to perceptually more preferred results, compared with the conventional methods.

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“…Current speech conversion algorithms were mainly designed for normal speech conversion (Abe et aI., 1988;Shikano et aI., 1991;Savic and Nam, 1991;Valbret et al, 1992). These algorithms usually were evaluated on the basis of whether the converted speech sounded closer to the target than to the original speech.…”

mentioning

confidence: 99%

Speech conversion and its application to alaryngeal speech enhancement

Qi²

Proceedings of Third International Conference on Signal Processing (ICSP'96)

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This manuscript ,has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer.The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedtbrough, substandard margins, and improper alignment can adversely affect reproduction.In the unlikely. event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion.Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and conti m1 ing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book.

show abstract

Voice personality transformation

Cited by 21 publications

References 2 publications

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

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

Feature Selection-based Voice Transformation

Speech conversion and its application to alaryngeal speech enhancement

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