Voice conversion based on parameter transformation

Gutiérrez-Arriola, Juana M.; Hsiao, Yung-Sheng; Montero, Juan Manuel; Pardo, José Manuel; Childers, D. G.

doi:10.21437/icslp.1998-520

Cited by 4 publications

(8 citation statements)

References 4 publications

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“…The voice conversion algorithm is described in [6]. The inputs to the algorithm are the utterances of the source and target speakers.…”

Section: Voice Conversion Algorithmmentioning

confidence: 99%

“…As shown in [6] segmentation of the utterance is needed due to speech variability. So we split a utterance into segments, performing voice conversion for each segment.…”

Section: Voice Conversion Algorithmmentioning

confidence: 99%

“…Each unit from the basic speaker is aligned with the same unit from the desired speaker. For a given parameter a correspondence between the original and the desired speaker is obtained and it is approximated by a linear function using a linear regression algorithm [6].…”

Section: Introductionmentioning

confidence: 99%

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A new multi-speaker formant synthesizer that applies voice conversion techniques

Gutiérrez-Arriola¹,

Montero²,

Vallejo³

et al. 2001

7th European Conference on Speech Communication and Technology (Eurospeech 2001)

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We present a multi-speaker formant synthesizer based on parameter concatenation. The user can choose among three speakers, two males and one female. The synthesizer stores all the parameters for the basic speaker and linear transformation functions to synthesized the other two. The complete database for one speaker consists of 455 parameterized units (diphones, triphones,...) and the parameters used are pitch, formants and bandwidths and source parameters (four parameters for the LF model, and glottal noise). To get the converted speaker we store a linear transformation function for each spectral stable segment of each unit. Preliminary results show that the quality of the synthesizer is very good and that this system can help us to study and understand the speaker variability problem.

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“…The voice conversion algorithm is described in [6]. The inputs to the algorithm are the utterances of the source and target speakers.…”

Section: Voice Conversion Algorithmmentioning

confidence: 99%

“…As shown in [6] segmentation of the utterance is needed due to speech variability. So we split a utterance into segments, performing voice conversion for each segment.…”

Section: Voice Conversion Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

A new multi-speaker formant synthesizer that applies voice conversion techniques

Gutiérrez-Arriola¹,

Montero²,

Vallejo³

et al. 2001

7th European Conference on Speech Communication and Technology (Eurospeech 2001)

View full text Add to dashboard Cite

show abstract

“…In voice conversion, speech produced by a source talker is modified to sound as if produced by a target talker. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] Because CI users may better understand or prefer speech produced by a particular gender or talker, voice conversion may benefit CI users by opti-mizing the acoustic input according to their preferred gender or talker profile, potentially allowing for improved speech comprehension. [20][21] In the source-filter model of speech production, acoustic output is modeled as an excitation (from the larynx) that undergoes filtering by the vocal tract superior to the larynx.…”

Section: Introductionmentioning

confidence: 99%

Voice conversion in cochlear implantation

et al. 2013

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Objectives/Hypothesis Voice conversion algorithms may benefit cochlear implant (CI) users who better understand speech produced by one talker than by another. It is unclear how the source or target talker's fundamental frequency (F0) information may contribute to perception of converted speech. This study evaluated voice conversion algorithms for CI users in which the source or target talker's F0 was included in the converted speech. Study Design Development and evaluation of computerized voice conversion algorithms in CI patients. Methods A series of cepstral-analysis based algorithms were developed and evaluated in six CI users. The algorithms converted talker voice gender (male-to-female, or female-to-male); either the source or target talker F0 was included in the converted speech. The voice conversion algorithms were evaluated in terms of recognition of IEEE sentences, speech quality, and voice gender discrimination. Results For both IEEE sentence recognition and voice quality ratings, performance was poorer with the voice conversion algorithms than with original speech. Performance on female-to-male conversion was superior to male-to-female conversion. Voice gender recognition performance showed that listeners strongly cued to the F0 that was included within the converted speech. Conclusion Limitations on spectral channel information experienced by CI users may result in poorer performance with voice conversion algorithms due to distortion of speech formant information and degradation of the spectral envelope. The strong cueing to F0 within the voice conversion algorithms suggests that CI users are able to utilize temporal periodicity information for some pitch-related tasks.

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“…Esto se hace mediante un algoritmo de regresión lineal. El algoritmo presenta una mejora en [Gut98] donde, para tratar las variaciones que se producen en una frase larga, se aplica el algoritmo por segmentos estables desde el punto de vista espectral.…”

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Síntesis multilocutor aplicando técnicas de conversión de hablante

Arriola¹

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Voice conversion technologies transform the voice uttered by a person (source speaker) in order that it is perceived as if another person had said it (target speaker). Traditionally the transformation is applied to the natural speech or to the synthetic speech as a post-processing block for a synthesizer. The aim of this thesis is to study the technologies in the state of the art and to incorporate them in a system of synthesis of voice. To achieve this goal it is necessary to know thoroughly the method of synthesis to use, study and develop the technology that better adapts to its characteristics. Two types of synthesizer have been studied:1. Formant synthesizer that concatenates parametrized units. In this case the parameters that are used for synthesis are the first five formants and four glottal source parameters of the model LF(Liljencrants and Fant).2. The second one is a LP(Linear Prediction) synthesizer that concatenates codified units. The source model is a polynomial of sixth order to shape the integral of the LP residue and a later filter to enhace high frequencies.In both cases tools have been developed or modified to analize 455 units corresponding to four speakers: two men and two women.The voice conversion techniques develop in each synthesizer are:1. A linear transformation is applied to convert the formants and we copy the LF model parameters of the target speaker.2. In case of the LP synthesizer the technology used for transformation is codebook mapping.The relevancy of the parameters used in the formant synthesizer has been studied as far as speaker identity is concerned. The conclusion of the above mentioned study indicates that the information about the speaker identity is distributed among all the analyzed parameters being the most relevant the fundamental frequency, F0, and the formant frequencies.The source model of the LP synthesizer has been modified to favor speaker transformation. There has been verified that the proposed source model supports a equivalent quality to the synthesizer that uses codified units CELP (Coded Excited Linear Prediction).Objective and subjective tests have been carried out to evaluate the ability to transform the speaker and the quality of the synthesized voice. There is demonstrated that the used technologies are efficient at the moment of changing the identity of the speaker but it is also observed a degradation of the quality of the synthetic voice.

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Voice conversion based on parameter transformation

Cited by 4 publications

References 4 publications

A new multi-speaker formant synthesizer that applies voice conversion techniques

A new multi-speaker formant synthesizer that applies voice conversion techniques

Voice conversion in cochlear implantation

Síntesis multilocutor aplicando técnicas de conversión de hablante

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