King, S.

Valentini-Botinhao, C.

Yamagishi, J.

English

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     Edinburgh Research Explorer                                      Using an intelligibility measure to create noise robust cepstralcoefficients for HMM-based speech synthesisCitation for published version:Valentini-Botinhao, C, Yamagishi, J & King, S 2012, 'Using an intelligibility measure to create noise robustcepstral coefficients for HMM-based speech synthesis'. in Proc. LISTA Workshop: Edinburgh, UK.Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Author final version (often known as postprint)Published In:Proc. LISTA WorkshopGeneral rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 20. Feb. 2015Using an intelligibility measure to create noise robust cepstral coefficientsfor HMM-based speech synthesisCassia Valentini-Botinhao1, Yan Tang2, Junichi Yamagishi1, Simon King11 The Centre for Speech Technology Research, University of Edinburgh, UK2Language and Speech Laboratory, Universidad del Paı´s Vasco, SpainC.Valentini-Botinhao@sms.ed.ac.uk, y.tang@laslab.org,jyamagis@inf.ed.ac.uk, Simon.King@ed.ac.ukAbstractThe aim of this work is to increase intelligibility of HMM-based synthetic speech in noisy environments by modifyingclean synthetic speech given that noise is known. For that pur-pose we need a measure for intelligibility of speech in noise thatcan automatically define the sort of modifications that we needto apply. In previous experiments [1] we have observed thatspectrum envelope modifications can have a significant posi-tive impact on the intelligibility of HMM-generated syntheticspeech in noise and that the Glimpse proportion measure (GP)[2] is highly correlated with subjective scores under those cir-cumstances.We have then introduced a method for cepstral coefficientextraction that modifies spectrum envelope based on the GPmeasure. The GP accounts only for the effect of additive noise,not requiring a reference unmodified speech signal to producea intelligibility prediction. To control the amount of distor-tions introduced by the modification we extract cepstral coef-ficients using an optimization criterion with two terms. Thefirst term accounts for the minimization of the mismatch be-tween natural speech periodogram and magnitude spectrum asmodeled by cepstral coefficient, the current criterion used forcepstral coefficient extraction performed at the training stage ofthe HMM-based speech synthesis framework [3]. The secondterm accounts for the maximization of an approximated analyt-ical and differentiable version of the GP measure. Using thismethod we found significant intelligibility gains however notfor all tested noise types which indicates that we need a moreeffective method for controlling distortions [4].0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000510152025303540455055Freq. (Hz)Sound Pressure Level (dB)  noiseoriginalless coefficientsall coefficientsFigure 1: Long term average spectrum of the original, modi-fied less coefficients (8 coefficients) and modified all coefficients(59) for speech-shaped noise.N N−M59 N−M10 N−M2203040506070Word accuracy rate (%)Figure 2: Word accuracy scores: original (N) and proposedwhen all (N-M59), 10 (N-M10) and 2 (N-M2) coefficients weremodified.In this work we propose to limit the frequency resolutionof the modifications, and therefore the amount of distortions,by altering only the first few cepstral coefficients, known tobe responsible for the coarse frequency resolution of the spec-trum. Fig.1 shows the long term average spectrum of originaland modified speech, where we can see the effect that limitingthe degrees for freedom has on the spectrum envelope. Listen-ing experiments results as shown in Fig.2 indicates that whenwe modify less coefficients we can improve intelligibility evenfurther.1. References[1] C. Valentini-Botinhao, J. Yamagishi, and S. King, “Canobjective measures predict the intelligibility of modifiedHMM-based synthetic speech in noise?” in Proc. Inter-speech, Florence, Italy, August 2011.[2] M. Cooke, “A glimpsing model of speech perception innoise,” J. Acoust. Soc. Am., vol. 119, no. 3, pp. 1562–1573,2006.[3] K. Tokuda, T. Kobayashi, and S. Imai, “Adaptive cepstralanalysis of speech,” IEEE Trans. Speech and Audio Pro-cessing, vol. SA-3, no. 6, pp. 481–489, Nov. 1995.[4] C. Valentini-Botinhao, R. Maia, J. Yamagishi, S. King, andH. Zen, “Cepstral analysis based on the Glimpse proportionmeasure for improving the intelligibility of HMM-basedsynthetic speech in noise,” in Proc. ICASSP, Kyoto, Japan,March 2012.

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Can objective measures predict the intelligibility of modiﬁed HMM-based synthetic speech in noise?” in

Cepstral analysis based on the Glimpse proportion measure for improving the intelligibility of HMM-based synthetic speech in noise,” in

     Edinburgh Research Explorer                                      Using an intelligibility measure to create noise robust cepstralcoefficients for HMM-based speech synthesisCitation for published version:Valentini-Botinhao, C, Yamagishi, J & King, S 2012, Using an intelligibility measure to create noise robustcepstral coefficients for HMM-based speech synthesis. in Proc. LISTA Workshop: Edinburgh, UK.Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Peer reviewed versionPublished In:Proc. LISTA WorkshopGeneral rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 05. Apr. 2019Using an intelligibility measure to create noise robust cepstral coefficientsfor HMM-based speech synthesisCassia Valentini-Botinhao1, Yan Tang2, Junichi Yamagishi1, Simon King11 The Centre for Speech Technology Research, University of Edinburgh, UK2Language and Speech Laboratory, Universidad del Paı´s Vasco, SpainC.Valentini-Botinhao@sms.ed.ac.uk, y.tang@laslab.org,jyamagis@inf.ed.ac.uk, Simon.King@ed.ac.ukAbstractThe aim of this work is to increase intelligibility of HMM-based synthetic speech in noisy environments by modifyingclean synthetic speech given that noise is known. For that pur-pose we need a measure for intelligibility of speech in noise thatcan automatically define the sort of modifications that we needto apply. In previous experiments [1] we have observed thatspectrum envelope modifications can have a significant posi-tive impact on the intelligibility of HMM-generated syntheticspeech in noise and that the Glimpse proportion measure (GP)[2] is highly correlated with subjective scores under those cir-cumstances.We have then introduced a method for cepstral coefficientextraction that modifies spectrum envelope based on the GPmeasure. The GP accounts only for the effect of additive noise,not requiring a reference unmodified speech signal to producea intelligibility prediction. To control the amount of distor-tions introduced by the modification we extract cepstral coef-ficients using an optimization criterion with two terms. Thefirst term accounts for the minimization of the mismatch be-tween natural speech periodogram and magnitude spectrum asmodeled by cepstral coefficient, the current criterion used forcepstral coefficient extraction performed at the training stage ofthe HMM-based speech synthesis framework [3]. The secondterm accounts for the maximization of an approximated analyt-ical and differentiable version of the GP measure. Using thismethod we found significant intelligibility gains however notfor all tested noise types which indicates that we need a moreeffective method for controlling distortions [4].0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000510152025303540455055Freq. (Hz)Sound Pressure Level (dB)  noiseoriginalless coefficientsall coefficientsFigure 1: Long term average spectrum of the original, modi-fied less coefficients (8 coefficients) and modified all coefficients(59) for speech-shaped noise.N N−M59 N−M10 N−M2203040506070Word accuracy rate (%)Figure 2: Word accuracy scores: original (N) and proposedwhen all (N-M59), 10 (N-M10) and 2 (N-M2) coefficients weremodified.In this work we propose to limit the frequency resolutionof the modifications, and therefore the amount of distortions,by altering only the first few cepstral coefficients, known tobe responsible for the coarse frequency resolution of the spec-trum. Fig.1 shows the long term average spectrum of originaland modified speech, where we can see the effect that limitingthe degrees for freedom has on the spectrum envelope. Listen-ing experiments results as shown in Fig.2 indicates that whenwe modify less coefficients we can improve intelligibility evenfurther.1. References[1] C. Valentini-Botinhao, J. Yamagishi, and S. King, “Canobjective measures predict the intelligibility of modifiedHMM-based synthetic speech in noise?” in Proc. Inter-speech, Florence, Italy, August 2011.[2] M. Cooke, “A glimpsing model of speech perception innoise,” J. Acoust. Soc. Am., vol. 119, no. 3, pp. 1562–1573,2006.[3] K. Tokuda, T. Kobayashi, and S. Imai, “Adaptive cepstralanalysis of speech,” IEEE Trans. Speech and Audio Pro-cessing, vol. SA-3, no. 6, pp. 481–489, Nov. 1995.[4] C. Valentini-Botinhao, R. Maia, J. Yamagishi, S. King, andH. Zen, “Cepstral analysis based on the Glimpse proportionmeasure for improving the intelligibility of HMM-basedsynthetic speech in noise,” in Proc. ICASSP, Kyoto, Japan,March 2012.

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Using an intelligibility measure to create noise robust cepstral coefficients for HMM-based speech synthesis

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