୍⯡♫ᅋἲே 㟁Ꮚ᝟ሗ㏻ಙᏛ఍                      ಙᏛᢏሗ‫ٻ‬‫ٻڇڮڞڤکڪڭگڞڠڧڠٻڡڪٻڠگڰگڤگڮکڤٻڠڣگ‬                     ৴ֶٕใ‫ٻۏۍۊۋۀڭٻۇڼھۄۉۃھۀگٻڠڞڤڠڤ‬ࣾஂ๏ਓ ిࢠ৘ใ௨৴ֶձ
‫ٻڮڭڠڠکڤڢکڠٻکڪڤگڜڞڤکڰڨڨڪڞٻڟکڜٻکڪڤگڜڨڭڪڡکڤ‬                TECHNICAL‫ٻڄڐڋڈڏڌڋڍڃڍڎڈڏڌڋڍګڮ‬THE INSTITUTE OF ELECTRONICS,REPORT OF IEICE.
‫ٻ‬INFORMATION AND COMMUNICATION ENGINEERSࡶԻ཈ѹ‫ͼٴ‬౷‫ܭ‬తԻ‫ݯ‬༧ଌʹ‫ͮ͘ج‬ϋΠϒϦουͳి‫ؾ‬Ի੠‫ڧ‬ௐ๏ͷධՁ޺†ాத‫ ాށ‬ஐ‫†ج‬άϥϜɾχϡʔϏοά†αΫϦΞχɾαΫςΟ†఩†தଜ† ಸྑઌ୺Պֶٕज़େֶӃେֶ৘ใՊֶ‫ڀݚ‬Պɼੜۨࢢ
E-mail: †{ko-t,tomoki,neubig,ssakti,s-nakamura}@is.naist.jp͋Β·͠޴಄ఠग़ऀͷͨΊͷ୅༻ൃ੠๏ͷҰͭʹɼి‫ࣜؾ‬ਓ޻޴಄Λ༻͍ͨൃ੠๏͕͋Γɼຊൃ੠๏ʹΑΓੜ੒͞

ΕΔԻ੠Λి‫ؾ‬Ի੠ͱ‫Ϳݺ‬ɽख़࿅ͷ޴಄ఠग़ऀ͸໌ྎੑͷߴ͍ి‫ؾ‬Ի੠Λੜ੒Ͱ͖Δɽ͔͠͠ͳ͕ΒɼࣗવͳԻ‫ݯ‬৴߸Λ֎෦͔Β‫ػ‬ցతʹੜ੒͢Δͷ͸ࠔ೉Ͱ͋Γɼಛʹࣗવͳ‫ج‬ຊप೾਺ύλʔϯΛੜ੒͢Δͷ͸ຊ࣭తʹ‫ۃ‬Ίͯࠔ೉ͳॲཧͱͳΔͨΊɼి‫ؾ‬Ի੠ͷࣗવੑ͸େ͖͘ྼԽ͢Δɽ·ͨɼి‫ࣜؾ‬ਓ޻޴಄͔Βੜ੒͞ΕΔԻ‫ݯ‬৴߸ࣗମ͕֎෦
ʹ࿙ΕΔͨΊɼࡶԻͱͯ͠ి‫ؾ‬Ի੠ʹࠞೖ͠ɼͦͷ඼࣭ΛྼԽͤ͞Δɽ͜ΕΒͷ໰୊ʹରॲ͢ΔͨΊɼզʑ͸ɼ໌ྎੑͷྼԽΛҾ͖‫ࣗʹͣ͜͞ى‬વੑΛେ෯ʹվળ͢Δ‫ڧ‬ௐ๏ͱͯ͠ɼैདྷͷ‫ڧ‬ௐ๏Ͱ͋ΔࡶԻ཈ѹʹ‫ͮ͘ج‬εϖΫτϧ
ิਖ਼ॲཧͱ౷‫ܭ‬త੠࣭ม‫ͮ͘جʹ׵‬Ի‫ݯ‬ಛ௃ྔੜ੒ॲཧΛ૊Έ߹ΘͤͨϋΠϒϦου๏ΛఏҊͨ͠ɽఏҊ๏Ͱ͸ɼ౷‫ܭ‬తख๏ʹΑΔεϖΫτϧ͓Αͼ༗੠ແ੠৘ใͷม‫ॲ׵‬ཧΛճආ͢Δ͜ͱͰɼม‫ʹࠩޡ׵‬ΑΔ໌ྎੑྼԽΛ๷͙ɽ·ͨɼ౷‫ܭ‬తख๏ʹΑΓɼ௨ৗԻ੠ͷ‫ج‬ຊप೾਺ύλʔϯ΁Λੜ੒͢Δ͜ͱͰɼࣗવੑΛେ෯ʹվળ͢ΔɽຊใࠂͰ͸ɼఏҊ๏ʹ͍ͭͯड़΂Δͱͱ΋ʹɼ࣮‫ݧ‬తධՁ݁Ռ͔Βͦͷ༗ޮੑΛࣔ͢ɽΩʔϫʔυൃ੠ো֐ɼి‫ؾ‬Ի੠ɼεϖΫτϧ‫ࢉݮ‬ɼ౷‫ܭ‬త੠࣭ม‫׵‬ɼϋΠϒϦου๏An Evaluation of a Hybrid Approach to Electrolaryngeal Speech
Enhancement Based on Noise Reduction and Statistical Excitation
Prediction
Kou TANAKA† , Tomoki TODA† , Graham NEUBIG† , Sakriani SAKTI† , and Satoshi
NAKAMURA†† Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama-cho,Ikoma-shi, 630-0101, Japan
E-mail: †{ko-t,tomoki,neubig,ssakti,s-nakamura}@is.naist.jp
Abstract This report presents our recent work on the development of a hybrid approach to improving naturalness of electrolaryngeal (EL) speech while minimizing degradation in intelligibility. An electrolarynx is a device
that artiﬁcially generates excitation sounds to enable laryngectomees to produce EL speech. Although proﬁcientlaryngectomees can produce quite intelligible EL speech, it sounds very unnatural due to the mechanical excitation
produced by the device. Moreover, the excitation sounds produced by the device often leak outside, adding noise
to EL speech. To address these issues, we propose a hybrid method using a noise reduction method for enhancing
spectral parameters and a voice conversion method for predicting excitation parameters. The experimental results
demonstrate that the proposed method yields signiﬁcant improvements in naturalness compared with EL speech
while keeping intelligibility high enough.
Key words speaking-aid, electrolaryngeal speech, spectral subtraction, voice conversion, hybrid approach

1. · ͑ ͕ ͖Ի੠͸ɼਓʑ͕͓‫ʹ͍ޓ‬ίϛϡχέʔγϣϯΛऔΔ͏͑Ͱɼ‫ج‬ຊతͳखஈͷ 1 ͭͰ͋Δɽ͔͠͠ͳ͕Βɼ޴಄ఠग़ऀ͸Ի੠Λࣗવͳ‫ൃͰܗ‬੠͢Δ͜ͱ͕೉͍͠ɽ௨ৗͷԻ੠ੜ੒աఔͰ͸ɼഏ͔Βͷ‫ʹؾݺ‬ΑΓ੠ଳΛৼಈͤ͞Δ͜ͱͰԻ‫ݯ‬৴߸Λੜ੒͠ɼ—1—This article is a technical report without peer review, and its polished and/or extended version may be published elsewhere.

Copyright ©2014 by IEICE 

Nasal cavity
Oral cavity

Vocal folds
Trachea

Excitation sounds
leaked outside

Tracheostoma   


 


 

 


Electrolarynx
Expired air

 



   Expired air

 
ਤ 1 Speech production mechanisms of non-disabled people (left 
  

ﬁgure) and total laryngectomees (right ﬁgure).

ͦΕΛௐԻ͢Δ͜ͱͰԻ੠Λੜ੒͢Δʢਤ 1 ࠨΛࢀরʣɽҰํͰɺ޴಄ఠग़ऀ͸ɼଟ͘ͷ৔߹੠ଳΛఠग़͢ΔͨΊɼԻ‫ݯ‬ੜ੒‫ػ‬ೳΛࣦ͏ɽͦͷͨΊɼ੠ଳৼಈΛ༻͍ͣʹԻ‫ݯ‬৴߸Λੜ੒͢
Δൃ੠๏͕ඞཁͱͳΓɼਂࠁͳൃ੠ো֐Λ‫͏ױ‬ɽ޴಄ఠग़ऀͷͨΊͷ୅༻ൃ੠๏ͷҰͭͱͯ͠ɼి‫ࣜؾ‬ਓ޻޴಄Λ༻͍ͨൃ੠๏͕͋Δɽਤ 1 ӈʹࣔ͢௨Γɼ֎෦͔Βੜ੒͞ΕͨԻ‫ݯ‬৴߸͕੠ಓ಺ʹ఻ୡ͠ɼௐԻ͞ΕΔ͜ͱͰԻ੠͕ੜ੒
͞ΕΔɽຊߘͰ͸ɼ͜ͷ୅༻ൃ੠๏Ͱੜ੒͞ΕΔԻ੠Λి‫ؾ‬Ի੠ͱ‫Ϳݺ‬ɽి‫ࣜؾ‬ਓ޻޴಄Λ༻͍ͨൃ੠๏͸ɼ1) मಘ͕༰қͰ
͋Δɼ2) ൃ੠࣌ʹ਎ମ΁ͷෛ୲͕গͳ͍ɼ3) ଞͷ୅༻ൃ੠๏ͱ
ൺֱ͠ɼൺֱత໌ྎੑͷߴ͍Ի੠Λੜ੒Ͱ͖Δɼͱ͍ͬͨར఺
͕͋ΔɽҰํͰɼ1) ి‫ࣜؾ‬ਓ޻޴಄ͷԻ‫ݯ‬৴߸ࣗମ͕֎෦ʹ࿙Εग़͠ɼࡶԻͱͯ͠ి‫ؾ‬Ի੠ʹࠞೖ͢ΔͨΊɼి‫ؾ‬Ի੠ͷ඼࣭͕ྼԽ͢Δɼ2) ࣗવͳԻ‫ݯ‬৴߸Λ֎෦͔Β‫ػ‬ցతʹੜ੒͢Δͷ͸ࠔ೉Ͱ͋Γɼి‫ؾ‬Ի੠ͷࣗવੑ͸ஶ͘͠௿Լ͢Δɼͱ͍ͬͨܽ఺͕͋Δɽಛʹɼ2 ͭ໨ͷܽ఺ʹؔͯ͠͸ɼൃ࿩಺༰ʹԊͬͨࣗવͳ‫ج‬ຊप೾਺ʢFundamental frequency; F0 ʣύλʔϯΛ࣋ͭԻ‫ݯ‬৴߸Λ֎෦͔Β‫ػ‬ցతʹੜ੒͢Δඞཁ͕͋Γɼຊ࣭తʹ‫ۃ‬Ίͯࠔ೉ͳॲཧͱͳΔɽ͜ΕΒͷ໰୊ʹରॲ͢ΔͨΊɼैདྷͷి‫ؾ‬Ի੠‫ڧ‬ௐ๏ͱͯ͠ɼࡶԻ཈ѹʹ‫ͮ͘ج‬εϖΫτϧิਖ਼๏ [1] ΍ɼ౷‫ܭ‬త੠࣭ม‫[ ׵‬2] [3] ʹ‫ͮ͘ج‬௨ৗԻ੠΁ͷม‫׵‬๏ [4] [5] ͳͲ͕ఏҊ͞Ε͍ͯΔɽલऀ͸ɼి‫ؾ‬Ի੠ʹࡶԻͱͯࠞ͠ೖ͢ΔԻ‫ݯ‬৴߸ͷӨ‫ڹ‬Λ؇࿨͢Δ͜ͱ͕Ͱ͖Δ͕ɼ‫ػ‬ցతͳԻ‫ݯ‬৴߸ʹ‫ى‬Ҽ͢Δి‫ؾ‬Ի੠ͷԻ‫ڹ‬తಛ௃͸มԽ͠ͳ͍ͨΊɼࣗવੑվળޮՌ͸‫ݶ‬ఆ͞ΕΔɽҰํɼ‫ऀޙ‬͸ɼి‫ؾ‬Ի੠͓Αͼ௨ৗԻ੠ͷಉҰൃ࿩σʔλ͔ΒಘΒΕΔ౷‫ྔܭ‬Λ༻͍ͯɼి‫ؾ‬Ի੠ͷεϖΫτϧಛ௃ྔ͔Β௨ৗԻ੠ͷεϖΫτϧಛ௃ྔ͓ΑͼԻ‫ݯ‬ಛ௃ྔ΁ͷม‫׵‬Λߦ͏ɽϑΥϧϚϯτૢ࡞ [6] ͳͲͷଞͷ৴߸ॲཧʹ‫ͮ͘ج‬ख๏ͱൺ΂ͯɼෳࡶͳม‫ॲ׵‬ཧ͕ՄೳͱͳΓɼΑΓࣗવԻ੠ʹ͍ۙεϖΫτϧಛ௃ྔ͕ಘΒΕΔɽ·ͨɼൃ࿩಺༰ʹԊͬͨΑΓࣗ
વͳ F0 ύλʔϯΛ༧ଌ͢Δ͜ͱͰɼΑΓࣗવͳԻ‫ݯ‬৴߸Λੜ੒͢Δ͜ͱ͕Ͱ͖Δɽͦͷ݁Ռɼి‫ؾ‬Ի੠ͷࣗવੑ͸ஶ͘͠޲
্͢ΔɽҰํͰɼগͳ͔Βͣม‫͕ࠩޡ׵‬ੜ͡ΔͨΊɼ໌ྎੑ͸ए‫ྼׯ‬Խ͢Δ܏޲͕͋Δ [5]ɽຊ‫Ͱڀݚ‬͸ɼ໌ྎੑͷྼԽΛҾ͖‫ࣗʹͣ͜͞ى‬વੑΛେ෯ʹվળ͢Δి‫ؾ‬Ի੠‫ڧ‬ௐ๏ͱͯ͠ɼࡶԻ཈ѹʹ‫ͮ͘ج‬εϖΫτϧ
ิਖ਼ॲཧͱ౷‫ܭ‬త੠࣭ม‫ͮ͘جʹ׵‬Ի‫ݯ‬ಛ௃ྔੜ੒ॲཧΛ૊Έ߹ΘͤͨϋΠϒϦου‫ڧ‬ௐ๏ΛఏҊ͢ΔɽఏҊ๏Ͱ͸ɼม‫ޡ׵‬ࠩͷӨ‫ڹ‬Λ‫ݮ‬Βͨ͢Ίʹɼ౷‫ܭ‬త੠࣭ม‫ʹ׵‬ΑΓม‫͢׵‬ΔԻ‫ڹ‬ಛ௃ྔΛ‫ݶ‬ఆ͢Δɽ۩ମతʹ͸ɼԻ‫ݯ‬ಛ௃ྔʹ͍ͭͯ͸౷‫ܭ‬త੠࣭ม‫ʹ׵‬ΑΓਪఆ͠ɼεϖΫτϧಛ௃ྔʹ͍ͭͯ͸ࡶԻ཈ѹʹ‫ิͮ͘ج‬ਖ਼ॲཧΛ༻͍Δɽ͞Βʹɼม‫ࠩޡ׵‬ͷӨ‫ڹ‬Λ௿‫͞ݮ‬
ͤΔͨΊɼ࿈ଓ F0 ύλʔϯਪఆॲཧΛಋೖ͢Δɽ٬‫؍‬ධՁ࣮‫͓ݧ‬Αͼओ‫؍‬ධՁ࣮‫ݧ‬ͷ݁Ռ͔ΒɼఏҊ๏ʹΑΓɼి‫ؾ‬Ի੠ͷ 

ਤ 2 EL speech enhancement based on SS.

໌ྎੑΛอͪͳ͕ΒɼࣗવੑΛେ෯ʹվળͰ͖Δ͜ͱΛࣔ͢ɽ

2. ి‫ؾ‬Ի੠ʹର͢ΔࡶԻ཈ѹʹ‫ͮ͘ج‬εϖΫτϧิਖ਼๏ి‫ؾ‬Ի੠ʹରͯ͠ɼࡶԻͱͯࠞ͠ೖ͢Δۭ‫ؾ‬தʹ࿙Εग़ͨ͠ి‫ࣜؾ‬ਓ޻޴಄ͷԻ‫ݯ‬৴߸ࣗମΛআ‫͢ڈ‬ΔͨΊʹɼదԠϑΟϧλΛ༻͍ͨࡶԻ཈ѹ [7] ΍εϖΫτϧ‫ॲࢉݮ‬ཧʢSpectral subtraction; SSʣ [8] Λ༻͍ͨࡶԻ཈ѹͳͲ͕ఏҊ͞Ε͍ͯΔɽ͜͜Ͱ͸ɼඇৗʹ؆ܿͳΞϧΰϦζϜͳ͕Β΋ɼߴ͍ࡶԻ཈ѹੑೳΛൃ‫͢ش‬ΔεϖΫτϧ‫ॲࢉݮ‬ཧΛ༻͍ͨࡶԻ཈ѹʹ‫ͮ͘ج‬εϖΫτϧิਖ਼๏ [1] ʹ͍ͭͯड़΂Δɽ࣌ؒ t ʹ͓͚Δ‫؍‬ଌ৴߸ Y (t) ͸ҎԼͰ‫ه‬ड़͞ΕΔɽ

Y (t) = S(t) + L(t)(1)͜͜ͰɼS ͸Ի੠৴߸ɼL ͸ࡶԻ৴߸Λද͢ɽ୹࣌ؒ཭ࢄϑʔϦΤม‫ʹ׵‬ΑΓಘΒΕΔ࣌ؒ t ͔ͭप೾਺ ω ͷ࣌ؒप೾਺ද‫ݱ‬Y (ω, t) ͸ɼҎԼͰ‫ه‬ड़͞ΕΔɽ

Y (ω, t) = S(ω, t) + L(ω, t)(2)

ҰൠԽεϖΫτϧ‫ࢉݮ‬๏ [9] Ͱ͸ɼࡶԻ৴߸ L ͷఆৗੑΛԾఆ͠ɼਪఆ͞ΕͨࡶԻͷৼ෯εϖΫτϧͷ‫ظ‬଴஋Λ‫؍‬ଌ৴߸ͷৼ෯εϖΫτϧ͔Β‫͢ࢉݮ‬Δ͜ͱʹΑΓɼࡶԻ͕཈ѹ͞Εͨ৴߸ͷৼ෯εϖΫτϧ |Ŝ(ω, t)| Λ࣍ࣜʹͯ‫ٻ‬ΊΔɽ

γ
1
< α+β
)
|Y (ω, t)|γ − α|L̂(ω)|γ ( |Y|L̂(ω)|γ(ω,t)|γ|Ŝ(ω, t)| =(3)γβ|L̂(ω)|(otherwise)͜͜Ͱɼα(α > 1) ͸ա‫ࢉݮ‬ύϥϝʔλɼβ(0 <= 1) ͸ϑϩ
=β<ΞϦϯά‫౓ڧ‬ͷ੍‫ޚ‬ύϥϝʔλɼγ ͸ࢦ਺ύϥϝʔλͰ |L̂(ω)|͸ਪఆ͞ΕͨࡶԻͷৼ෯εϖΫτϧͷ‫ظ‬଴஋ͱ͢Δɽ௨ৗɼແ
Ի۠ؒͷࡶԻͷৼ෯εϖΫτϧ |L(ω, t)| Λ࣌ؒฏ‫ͨ͠ۉ‬΋ͷΛɼશ۠ؒͰͷࡶԻৼ෯εϖΫτϧͷϓϩτλΠϓ L̂(ω) ͱͯ͠ར༻͢ΔɽҰํͰɼి‫ؾ‬Ի੠‫ڧ‬ௐʹ͓͍ͯ͸ɼࡶԻ৴߸͸ి‫ࣜؾ‬ਓ޻޴಄͔Βੜ੒͞ΕΔԻ‫ݯ‬৴߸Ͱ͋ΔͨΊɼࣄલʹͦͷৼ෯ಛੑΛ‫ٻ‬Ί͓ͯ͘͜ͱ͕ՄೳͰ͋Δɽຊใࠂʹ͓͚Δ‫ڧ‬ௐॲཧաఔΛਤ 2 ʹࣔ͢ɽి‫ࣜؾ‬ਓ޻޴಄Λ௨ৗ௨Γ޴‫ʹݩ‬ԡ͠౰ͯͯԻ‫ݯ‬৴߸Λੜ੒ͨ͠ࡍʹɼ‫ݩޱ‬ͷϚΠΫͰ‫؍‬ଌ͞ΕΔࡶԻ৴߸Λࣄલʹऩ࿥͢Δɽͦͷࡍʹɼి‫ؾ‬Ի੠ࣗମ͕ऩ࿥͞Εͳ͍Α͏ʹ‫ޱ‬͸ด͓ͯ͘͡ɽಘΒΕͨࡶԻ৴߸͔Βৼ෯εϖΫτϧͷ‫ظ‬଴஋Λ‫͠ࢉܭ‬ɼࡶԻৼ෯εϖΫτϧͷϓϩτλΠϓ |L(ω, t)| Λ‫ٻ‬ΊΔɽεϖΫτϧ‫ॲࢉݮ‬ཧͰ—2—͸ɼա‫ࢉݮ‬ύϥϝʔλ͸ α = 2 ͱ͠ɼϑϩΞϦϯά‫౓ڧ‬ͷ੍‫ޚ‬ύϥϝʔλ͸ β = 0ɼࢦ਺ύϥϝʔλ͸ γ = 1 ͱ͢Δɽ$



$

3. ి‫ؾ‬Ի੠͔Β௨ৗԻ੠΁ͷ౷‫ܭ‬తख๏ʹ‫ͮج‬͘੠࣭ม‫׵‬๏ #P (X t , Y t |λ)
=

M


αm N



 
(X,Y ))[X , Σ(X,Yt , Y t ] ; μm
m





(5)

͜͜ͰɼN (·; μ, Σ) ͸ฏ‫ۉ‬ϕΫτϧ μɼ͓Αͼ‫ڞ‬෼ࢄߦྻ ΣΛ࣋ͭਖ਼‫ن‬෼෍Ͱ͋Δɽ·ͨɼλ ͸ϞσϧύϥϝʔληοτΛ)͓Αͼද͠ɼ֤෼෍ m ͷࠞ߹ॏΈ αm ɼฏ‫ۉ‬ϕΫτϧ μ(X,Ym(X,Y )
Ͱߏ੒͞ΕΔɽ͜͜Ͱɼm ൪໨ͷ෼෍ʹ͓‫ڞ‬෼ࢄߦྻ Σm(X,Y ))
͓Αͼ‫ڞ‬෼ࢄߦྻ Σm͸͍࣍ࣜͯɼฏ‫ۉ‬ϕΫτϧ μ(X,YmͰද͞ΕΔɽ




(XX)
(XY )
Σm
μ(X)
Σm
(X,Y )
)
m
μ(X,Y
=
=
,
Σ
(6)
m
m
(Y X)
(Y Y )
)
μ(Y
Σm
Σm
m
(Y )
͜͜Ͱɼμ(X)m ͓Αͼ μm ͸ೖྗಛ௃ྔ͓Αͼग़ྗಛ௃ྔͷ(XX)(Y Y )͓Αͼ Σm͸ೖྗಛ௃ྔ͓Αฏ‫ۉ‬ϕΫτϧΛද͠ɼΣm
(XY )
(Y X)
͓Αͼ Σm͸૬‫ڞޓ‬෼ͼग़ྗಛ௃ྔͷ‫ڞ‬෼ࢄߦྻɼΣmࢄߦྻΛද͢ɽి‫ؾ‬Ի੠ͷεϖΫτϧηάϝϯτಛ௃ྔʢϝϧέϓετϥϜηάϝϯτʣͱ௨ৗԻ੠ͷϝϧέϓετϥϜɼର਺ F0 ɼଳҬผฏ‫ ۉ‬AC ͷؒʹ͓͍ͯɼ‫ ܭ‬3 ͭͷ GMM Λֶश









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ਤ 3 EL speech enhancement based on VC.

͢Δɽ·ͨɼ௨ৗԻ੠ͷԻ‫ڹ‬ಛ௃ྔʢϝϧέϓετϥϜʣʹରͯ͠͸ɼ‫಺ྻܥ‬มಈʢGlobal variance; GVʣ [3] ͷ֬཰ີ౓ؔ਺΋(y)(y)ֶश͢Δɽ͜͜ͰɼGV ϕΫτϧ v (y) = [v1 , · · ·, vD(y) ] ͸ɼ௨ৗԻ੠ͷ੩తಛ௃ྔ‫ʹྻܥ‬ରͯ͠ɼ֤ൃ࿩͝ͱʹ࣍ࣜͰ‫ࢉܭ‬
͞ΕΔɽ
(y)

m=1

 #  (4)͜͜Ͱɼx ͸࣌ؒϑϨʔϜ t ʹ͓͚ΔεϖΫτϧಛ௃ྔΛද͠ɼຊ‫Ͱڀݚ‬͸ϝϧέϓετϥϜΛ༻͍ΔɽE ͓Αͼ f ͸֤ʑม‫׵‬ߦྻ͓ΑͼόΠΞεϕΫτϧΛද͠ɼֶशσʔλͷશϑϨʔϜʹ͓͚ΔεϖΫτϧಛ௃ྔʹର͢Δओ੒෼෼ੳʹΑΓ‫ٻ‬ΊΔɽ ͸సஔΛද͢ɽҰํͰɼ௨ৗԻ੠ͷ‫ݸ‬ʑͷԻ‫ڹ‬ಛ௃ྔͱͯ͠ɼ 
Y t = [y Λ࢖༻͢Δɽ͜͜Ͱɼಈతಛ௃ྔ Δy ͸
t , Δy t ]Δy t = y t − y t−1 ʹΑΓ‫͢ࢉܭ‬ΔɽεϖΫτϧಛ௃ྔͱͯ͠ϝϧέϓετϥϜΛ༻͍ɼԻ‫ݯ‬ಛ௃ྔͱͯ͠ର਺ F0 ͓ΑͼଳҬผฏ‫ ۉ‬AC [11] Λ༻͍Δɽ
ύϥϨϧσʔλʹରͯ͠ಈత࣌ؒ৳ॖʢDynamic time wrapping; DTWʣΛߦ͍ɼೖྗಛ௃ྔ X t ͱग़ྗಛ௃ྔ Y t ͷର Ԡ෇͚Λߦͬͨ݁߹ϕΫτϧ [X t , Y t ] Λ༻͍ͯɼ࣍ࣜʹࣔ͢ͱ͓Γɼ݁߹֬཰ີ౓ؔ਺Λࠞ߹ਖ਼‫ن‬෼෍Ϟσϧ (Gaussian
mixture model; GMM) ͰϞσϧԽ͢Δ [12]ɽ

 
   ౷‫ܭ‬తख๏ʹ‫ͮ͘ج‬੠࣭ม‫׵‬๏ʢStatistical voice conversion; VCʣʹ‫͖ͮج‬ɼి‫ؾ‬Ի੠͔Β௨ৗԻ੠΁ม‫͢׵‬Δ͜ͱ
Ͱɼి‫ؾ‬Ի੠‫ڧ‬ௐΛߦ͏ [4]ɽຊॲཧաఔΛਤ 3 ʹࣔ͢ɽֶश
෦ͱม‫׵‬෦͔Βߏ੒͞Ε͓ͯΓɼֶश෦Ͱ͸ɼి‫ؾ‬Ի੠͓Αͼ௨ৗԻ੠ͷಉҰൃ࿩σʔλ͔ΒಘΒΕΔ౷‫ྔܭ‬Λ༻͍ͯɼి‫ؾ‬Ի੠ͷεϖΫτϧηάϝϯτಛ௃ྔ͔Β௨ৗԻ੠ͷεϖΫτϧ
ಛ௃ྔ͓ΑͼԻ‫ݯ‬ಛ௃ྔͰ͋Δ F0 ͱඇप‫ظ‬੒෼ʢAperiodic
Components; ACʣ [10] ΁ͷม‫׵‬Ϟσϧ͕ɼ֤ʑࣄલʹֶश͞
ΕΔɽม‫׵‬෦Ͱ͸ɼಘΒΕͨม‫׵‬ϞσϧΛ༻͍ͯɼి‫ؾ‬Ի੠ͷεϖΫτϧηάϝϯτಛ௃ྔ͔Βɼ௨ৗԻ੠ͷεϖΫτϧಛ
௃ྔɼF0 ɼAC ͕ਪఆ͞ΕΔɽਪఆ͞ΕͨԻ‫ڹ‬ಛ௃ྔ͔ΒɼϘίʔμʹΑΓ‫ڧ‬ௐԻ੠৴߸Λੜ੒͢Δɽ
3. 1 ֶ श ॲ ཧ࣌ؒϑϨʔϜ t ʹ͓͚Δి‫ؾ‬Ի੠ͷεϖΫτϧηάϝϯτಛ௃ྔʢDx ࣍‫ݩ‬ϕΫτϧʣΛ X t ͱ͠ɼલ‫ ޙ‬C ϑϨʔϜͷ৘ใΛ༻͍ͯɼ࣍ࣜʹΑΓநग़͢Δɽ
X t = E[xt−C , ʜ, xt , ʜ, xt+C ] + f 

vd

=

T
T
1 
1 
(yt,d −
yγ,d )
T t=1
T γ=1

(7)

͜͜Ͱɼyt,d ͸ϑϨʔϜ t ͷ௨ৗԻ੠ͷ੩తಛ௃ྔϕΫτϧ y t
ͷ d ࣍‫ݩ‬໨ͷཁૉͰ͋ΔɽGV ͷ֬཰ີ౓ؔ਺ P (v (y) |λ(v) ) ͸ɼฏ‫ۉ‬ϕΫτϧ μ(v) ͓Αͼ‫ڞ‬෼ࢄߦྻ Σ(vv) ͷਖ਼‫ن‬෼෍Λ༻͍ͯɼҎԼͷΑ͏ʹϞσϧԽ͢Δɽ(8)
P (v (y) |λ(v) ) = N v (y) ; μ(v) , Σ(vv)3. 2 ॲཧաఔɿม‫׵‬෦ม ‫ ׵‬෦ Ͱ ͸ ɼ‫ݸ‬ʑͷ GMM Λ ༻ ͍ ͯ ɼ࠷ ໬ ‫ ྻ ܥ‬ม ‫׵‬๏ [3] ʹ Α Γɼి ‫ ؾ‬Ի ੠ ͷ ೖ ྗ ಛ ௃ ྔ ‫ ͔ ྻ ܥ‬Β ௨ ৗ Ի੠ ͷ ֤ ग़ ྗ ಛ ௃ ྔ ‫ ͱ ΁ ྻ ܥ‬ม ‫ ͢ ׵‬Δ ɽ࣌ ؒ ϑ Ϩ ʔ Ϝ 1
͔ Β T · Ͱ ͷ ి ‫ ؾ‬Ի ੠ ͓ Α ͼ ௨ ৗ Ի ੠ ͷ ‫ݸ‬ʑͷ ಛ ௃ =ྔ ‫ ྻ ܥ‬Λ ͦ Ε ͧ Ε X = [X 
1 , · · · , X t , · · · , X T ] ,Y 
[Y 1 , · · · , Y t ,· · · ,Y T ] ͱ͓͘ɽ͜ͷͱ͖ɼม‫ޙ׵‬ͷ੩తಛ ௃ྔ‫ ྻܥ‬ŷ = [ŷ 
1 , · · · , ŷ t , · · · , ŷ T ] ͸࣍ࣜͰ‫͞ࢉܭ‬ΕΔɽŷ = argmax P (Y |X, λ)ω P (v (y) |λ(v) )y—3—

subject to Y = W y(9)Training process
Noise

͜͜ͰɼW ͸੩తಛ௃ྔ‫ ྻܥ‬y Λ੩తɾಈతಛ௃ྔ‫ ྻܥ‬Y ʹ
ࣸ૾͢Δม‫ྻߦ׵‬ม‫ྻߦ׵‬Λද͢ɽ·ͨɼω ͸ GV ͷ໬౓ॏΈΛද͢ɽͳ͓ɼ௨ৗԻ੠ͷର਺ F0 ΁ͷม‫ॲ׵‬ཧͷࡍʹ͸ɼ༗੠ʗແ੠৘ใ΋ಉ࣌ʹਪఆ͢Δɽ͜ͷॲཧ͸ɼମ಺఻ಋແ੠Ի
੠͔Β௨ৗԻ੠΁ͷ౷‫ܭ‬తม‫׵‬๏ [13] ͰߦΘΕΔ΋ͷͱಉҰͰ͋Δɽม‫ޙ׵‬ͷ F0 ύλʔϯ͓Αͼ AC Λ༻͍ͯࠞ߹ྭৼ‫ݯ‬Ϟσϧ [11] ʹΑΓԻ‫ݯ‬৴߸Λੜ੒͢Δɽม‫ޙ׵‬ͷεϖΫτϧಛ௃ྔΛԻ‫ݯ‬৴߸ʹ৞ΈࠐΉ͜ͱͰɼ‫ڧ‬ௐԻ੠Λੜ੒͢Δɽ

Electrolaryngeal speech

Feature extraction
Spectral segment feature
Windowing+
Fouriertransform

Training

ofof

GMM for F0

GMM for AC

SplineinterpolationNoiseestimation

Log F0U/V information

4. εϖΫτϧิਖ਼‫ͼٴ‬౷‫ܭ‬తԻ‫ݯ‬ੜ੒ʹ‫ͮ͘ج‬ϋΠϒϦουి‫ؾ‬Ի੠‫ڧ‬ௐ๏
4. 1 ैདྷͷి‫ؾ‬Ի੠‫ڧ‬ௐ๏ͷར఺͓Αͼܽ఺ࡶԻ཈ѹʹ‫ͮ͘ج‬εϖΫτϧิਖ਼๏Ͱ͸ɼ֎෦ʹ࿙Εग़͢Ի‫ݯ‬৴߸ͷӨ‫ڹ‬Λ௿‫͖Ͱݮ‬Δ͕ɼԾʹ‫׬‬શͳࡶԻআ‫ॲڈ‬ཧ͕ߦ͑ͨͱͯ͠΋ɼ‫ڧ‬ௐ͞Εͨి‫ؾ‬Ի੠͸ґવͱͯ͠‫ػ‬ցతʹੜ੒͞ΕͨԻ‫ݯ‬৴߸ͷӨ‫ڹ‬Λ‫͘ڧ‬ड͚Δɽಛʹɼਓ޻తͳ F0 ύλʔϯͷӨ‫ڹ‬͸େ͖͘ɼࣗવੑͷஶ͍͠ྼԽ͸ຊ࣭తʹආ͚ΒΕͳ͍ɽ݁Ռͱͯ͠ɼิਖ਼ॲཧʹΑΔ඼࣭վળޮՌ͸‫ݶ‬ΒΕΔɽҰํͰɼຊॲཧͰੜ͡ΔѱӨ‫ڹ‬͸‫ۃ‬Ίͯখ͘͞ɼి‫ؾ‬Ի੠ͷൺֱతߴ͍໌ྎੑ΋ҡ࣋͞ΕΔɽ౷‫ܭ‬తख๏ʹ‫ͮ͘ج‬੠࣭ม‫׵‬๏ʹ‫ؾిͮ͘ج‬Ի੠‫ڧ‬ௐॲཧͰ͸ɼ௨ৗԻ੠ͷ౷‫ڧ͍ͯͮجʹྔܭ‬ௐԻ੠͕ੜ੒͞ΕΔɽεϖΫτϧಛ௃ྔͷΈͰͳ͘Ի‫ݯ‬ಛ௃ྔʹରͯ͠΋ߴ౓Ͱෳࡶͳม
‫ॲ׵‬ཧ͕ՄೳͱͳΔͨΊɼେ෯ͳࣗવੑվળޮՌ͕ಘΒΕΔɽ
ҰํͰɼ‫׬‬શʹࣗવͳ௨ৗԻ੠ͷԻ‫ڹ‬ಛ௃ྔ΁ͱม‫͢׵‬Δͷ͸ࠔ೉Ͱ͋Γɼগͳ͔Βͣม‫͕ࠩޡ׵‬ੜ͡Δɽ݁Ռͱͯ͠ɼ‫ڧ‬ௐԻ੠ͷ໌ྎੑ͸ి‫ؾ‬Ի੠ͱൺֱͯ͠ए‫ྼׯ‬Խ͢Δ܏޲͕͋Δɽ
4. 2 ϋΠϒϦουి‫ؾ‬Ի੠‫ڧ‬ௐ๏໌ྎੑͷྼԽΛҾ͖‫ࣗʹͣ͜͞ى‬વੑΛେ෯ʹվળ͢Δి‫ؾ‬Ի੠‫ڧ‬ௐ๏ͱͯ͠ɼࡶԻ཈ѹʹ‫ͮ͘ج‬εϖΫτϧิਖ਼ॲཧͱ౷
‫ܭ‬త੠࣭ม‫ͮ͘جʹ׵‬Ի‫ݯ‬ಛ௃ྔੜ੒ॲཧΛ૊Έ߹ΘͤͨϋΠϒϦου๏ΛఏҊ͢ΔɽఏҊ๏ͷॲཧաఔΛਤ 4 ʹࣔ͢ɽ޴಄ఠग़ऀͷௐԻ‫׭ث‬͸ਖ਼ৗʹ‫ػ‬ೳ͢Δ৔߹͕ଟ͘ɼͦΕ‫ނ‬ʹɼൺֱత໌ྎੑͷߴ͍ి‫ؾ‬Ի੠ͷੜ੒͕ՄೳͱͳΔɽ͢ͳΘͪɼి‫ؾ‬Ի੠ͷεϖΫτϧಛ௃ྔʹؔͯ͠͸ɼੜ੒աఔͷ૬ҧ΍Ի‫ݯ‬৴߸ͷ֎෦࿙ΕͷӨ‫ڹ‬͸͋Δ΋ͷͷɼ௨ৗԻ੠ͷεϖΫτϧಛ௃ྔʹൺֱతྨࣅͨ͠΋ͷͱͳΔɽͦ͜Ͱɼి‫ؾ‬Ի੠ͷεϖΫτϧಛ௃ྔʹؔͯ͠͸࠷େ‫͢༻׆ʹݶ‬Δ͜ͱΛߟ͑ɼࡶԻ཈ѹʹ‫ͮ͘ج‬εϖΫτϧิਖ਼ॲཧʹΑΓಘΒΕΔεϖΫτϧ
ಛ௃ྔΛ༻͍Δɽ౷‫ܭ‬త੠࣭ม‫ͮ͘جʹ׵‬ख๏ͱൺֱ͢Δͱɼి‫ؾ‬Ի੠ͷ࣋ͭಠಛͷ‫ػ‬ցతͳ੠৭͸࢒Δ΋ͷͷɼม‫ࠩޡ׵‬ͷӨ‫ڹ‬Λճආ͢Δ͜ͱ͕Ͱ͖Δɽ·ͨɼ޴಄ఠग़ऀຊਓͷεϖΫτϧಛ௃ྔΛ࢖༻͢Δͱ͍͏ར఺΋͋Δɽ
ҰํͰɼి‫ؾ‬Ի੠ͷԻ‫ݯ‬ಛ௃ྔʹؔͯ͠͸ɼ‫׬‬શʹ‫ػ‬ցతʹੜ੒͞Εͨ΋ͷͰ͋Γɼ௨ৗԻ੠ͷԻ‫ݯ‬ಛ௃ྔͱ͸େ͖͘ҟͳΔɽຊ‫͍༻Ͱڀݚ‬Δి‫ࣜؾ‬ਓ޻޴಄ʹ͓͍ͯ΋ɼF0 ύλʔϯ͸ൃ੠۠ؒͰ΄΅ҰఆͰ͋ΓɼAC ͸Ұ؏ͯ͠‫͍ڧ‬΋ͷͱͳΔɽ
ͦ͜ͰɼԻ‫ݯ‬ಛ௃ྔͰ͋Δ F0 ͓Αͼ AC ʹؔͯ͠͸ɼి‫ؾ‬Ի੠͔Β௨ৗԻ੠΁ͷ౷‫ܭ‬త੠࣭ม‫׵‬๏ʹΑΓਪఆ͞Εͨ΋ͷΛ
༻͍Δɽ௨ৗԻ੠͔ΒಘΒΕΔ౷‫ྔܭ‬ͷ࢖༻ʹΑΓɼΑΓࣗવͳԻ‫ݯ‬ಛ௃ྔΛ࣋ͭ‫ڧ‬ௐԻ੠͕ಘΒΕΔɽಛʹɼF0 ύλʔϯʹؔͯ͠͸ɼ‫ݱ‬ঢ়ͷ౷‫ܭ‬తม‫ٕ׵‬ज़Ͱ͸े෼ʹߴ͍ਪఆਫ਼౓͸ಘΒΕͳ͍΋ͷͷɼ‫ݩ‬ͷి‫ؾ‬Ի੠͕࣋ͭਓ޻తͳ΋ͷͱൺֱ͢ΔͱɼΑΓࣗવԻ੠ʹ͍ۙ΋ͷ͕ಘΒΕΔɽ·ͨɼAC ʹؔͯ͠΋ɼԻӆʹԠͯ͡ΑΓࣗવʹมԽ͢Δ΋ͷ͕ಘΒΕΔɽ͜Ε

Training

Training

of

GMM for U/V

Aperiodic
components

Analysis

Normal speech

Averaged noise spectrum
Enhancement process

Electrolaryngeal speech
Feature extraction
Windowing+
Fouriertransform

Subtraction

Spectral segment feature

Conversion
with
GMM for U/VConversion
with
GMM for F0

Conversion
with
GMM for AC

U/V information

Log F0

Aperiodic
components

Parameterization
Mixed excitation generation
Spectral parameter

Excitation signal
Synthesis

Enhanced speech

ਤ 4 EL speech enhancement based on a hybrid approach.

ʹΑΓɼి‫ؾ‬Ի੠ͷࣗવੑΛେ෯ʹվળ͢Δ͜ͱ͕Ͱ͖Δɽ
4. 3 Ի‫ݯ‬ಛ௃ྔ༧ଌͷվળ
4. 3. 1 ࿈ଓ F0 ϞσϧͷಋೖʢCF0ʣແ੠۠ؒͰ͸ F0 ͕‫؍‬ଌͰ͖ͳ͍ͨΊɼF0 ύλʔϯ͸ෆ࿈ଓͳ΋ͷͱͳΔɽྫ͑͹ɼແ੠۠ؒʹ͓͚Δ F0 ͷ஋ͱͯ͠ɼ༗
੠۠ؒͰ‫؍‬ଌ͞ΕΔ஋ͱ͸໌Β͔ʹҟͳΔ஋ʢྫ͑͹ 0 ͳͲʣΛ༻͍Δ [13]ɽ͜ͷΑ͏ͳෆ࿈ଓͳ F0 ύλʔϯΛϞσϧԽ͢Δͷ͸༰қͰ͸ͳ͘ɼෳࡶͳϞσϧ͕ඞཁͱͳΔɽ͜Εʹରͯ͠ɼओʹ౷‫ܭ‬తύϥϝτϦοΫԻ੠߹੒ͷ෼໺ʹ͓͍ͯɼແ੠۠ؒʹ͓͍ͯ΋࿈ଓతͳ F0 ύλʔϯ͕‫؍‬ଌͰ͖Δ
΋ͷͱͯ͠ϞσϧԽΛߦ͏࿈ଓ F0 ʢContinuous F0 ; CF0ʣϞσϧ͕ఏҊ͞Ε͓ͯΓɼͦͷ༗ޮੑ͕ใࠂ͞Ε͍ͯΔ [14]ɽͦ
͜ͰɼຊใࠂͰ͸ɼF0 ύλʔϯ༧ଌʹ࿈ଓ F0 ϞσϧΛಋೖ͢Δɽແ੠۠ؒʹରͯ͠εϓϥΠϯิؒॲཧΛߦ͏͜ͱͰɼ࿈ଓ
తͳ F0 ύλʔϯΛੜ੒ͨ͠‫ʹޙ‬ɼGMM ʹΑΔϞσϧԽΛߦ͏ɽͳ͓ɼU/V ৘ใʹؔͯ͠͸ɼF0 ύλʔϯͱ͸ผͷ GMM
ʹΑΓϞσϧԽ͢Δɽ
4. 3. 2 ϚΠΫϩϓϩισΟͷআ‫ڈ‬ʢLPFʣ
௨ৗԻ੠͔Βநग़͞ΕΔ F0 ύλʔϯ্Ͱ͸ɼϚΠΫϩϓϩ
ισΟͱ‫ݺ‬͹ΕΔ‫ٸ‬फ़ͳมԽ͕͠͹͠͹‫؍‬ଌ͞ΕΔɽҰํͰɼϋΠϒϦουํࣜʹ͓͍ͯɼϚΠΫϩϓϩισΟΛਫ਼౓ྑ͘༧
ଌ͢Δͷ͸༰қͰ͸ͳ͘ɼΑΓෳࡶͳϞσϧ͕ඞཁͱͳΔɽͦ͜Ͱɼ‫ݱ‬ঢ়ͷϞσϧʢGMMʣͰ্ख͘ϞσϧԽͰ͖ͳ͍ϚΠ—4—

5. ࣮‫ݧ‬తධՁ
5. 1 ࣮ ‫ ݧ‬৚ ݅޴಄ఠग़ऀ 1 ໊ͷ EL Ի੠ͱɼ݈ৗऀ 1 ໊ͷ௨ৗԻ੠Λ༻͍Δɽֶशσʔλͱͯ͠ ATR Իૉόϥϯεจηοτதͷ 50 จத 40 จΛ༻͍ɼධՁσʔλͱͯ͠࢒Γͷ 10 จΛ༻͍ɼަࠩ‫ݕ‬ఆΛߦ͏ɽαϯϓϦϯάप೾਺͸ 16 kHzɼ෼ੳϑϨʔϜ௕͸
25 msɼ෼ੳϑϨʔϜγϑτ͸ 5 ms ͱ͢Δɽೖྗಛ௃ྔͱ͠
ͯɼ0ʙ24 ࣍ͷϝϧέϓετϥϜηάϝϯτಛ௃ྔʢલ‫ ޙ‬4 ϑϨʔϜʣΛ༻͍ΔɽεϖΫτϧ෼ੳ͸ EL Ի੠ʹରͯ͠͸ FFT෼ੳΛ༻͍ɼ௨ৗԻ੠ʹରͯ͠͸ STRAIGHT ෼ੳ [15] Λ༻͍ΔɽGMM ͷࠞ߹਺͸ 32ʢεϖΫτϧม‫༻׵‬ʣɼ16ʢඇप‫ظ‬੒෼ਪఆ༻ʣͱ͢ΔɽLPF ͷΧοτΦϑप೾਺͸ 10 Hz ͱ͢Δɽ٬‫؍‬ධՁ࣮‫Ͱݧ‬͸ɼֶशσʔλʹ͓͚Δ F0 ύλʔϯ͕ F0 ਪఆਫ਼౓ʹ༩͑ΔӨ‫ڹ‬Λௐࠪ͢ΔɽͦͷࡍʹɼF0 ਪఆ༻ GMM
ͷࠞ߹਺Λ 8ɼ16ɼ32ɼ64 ͱมԽͤ͞Δɽओ‫؍‬ධՁ࣮‫Ͱݧ‬͸ɼҎԼʹࣔ͢γεςϜͷԻ੠ʹ͍ͭͯɼࣗવੑʹؔ͢Δ̑ஈ֊ΦϐχΦϯධఆͱϓϦϑΝϨϯεςετɼ‫ྎ໌ͼٴ‬ੑʹؔ͢Δॻ͖औΓࢼ‫ݧ‬Λߦ͏ɽ
EL: ి‫ؾ‬Ի੠SS: ࡶԻ཈ѹʹ‫ͮ͘ج‬εϖΫτϧิਖ਼ॲཧԻ੠
VC: ౷‫ܭ‬తख๏ʹ‫ڧͮ͘ج‬ௐԻ੠
SS+VC: Ի‫ݯ‬ಛ௃ྔ༧ଌʹର͢ΔվળॲཧΛࢪ͍ͯ͠ͳ͍ϋΠϒϦου๏ʹ‫ڧͮ͘ج‬ௐԻ੠
SS+VC+CF0: ࿈ଓ F0 ύλʔϯΛߟྀͨ͠ϋΠϒϦου๏ʹ‫ڧͮ͘ج‬ௐԻ੠Hybrid (V): ൃ࿩۠ؒΛશͯ༗੠Իͱͨ͠ϋΠϒϦου๏ʹ‫ڧͮ͘ج‬ௐԻ੠Hybrid (U/V): VC ʹ‫ͮ͘ج‬ਪఆ U/V ৘ใΛ༻͍ͨϋΠϒϦου๏ʹ‫ڧͮ͘ج‬ௐԻ੠Hybrid (target U/V): ཧ૝తͳ U/V ৘ใΛ༻͍ͨϋΠϒϦου๏ʹ‫ڧͮ͘ج‬ௐԻ੠
͜͜ͰɼHybrid (V, U/V, target U/V) ʹ͓͚ΔϋΠϒϦου
ํࣜʹ͓͍ͯ͸ɼSS+VC ʹରͯ͠ CF0 ͓Αͼ LPF Λಋೖͨ͠΋ͷΛ༻͍Δɽ·ͨɼཧ૝తͳ U/V ৘ใ͸ɼVC ʹ‫ͮ͘ج‬EL ‫ڧ‬ௐԻ੠ͱ௨ৗԻ੠ͱͷؒͰ DTW Λߦ͏͜ͱͰಘΔɽඃ‫ऀݧ‬͸உੑ 5 ໊Ͱ͋Γɼ1 ਓ͋ͨΓ֤γεςϜʹ͖ͭ 10 αϯϓϧͷ‫ ܭ‬50 αϯϓϧΛडௌ͢Δɽ

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ΫϩϓϩισΟʹؔͯ͠͸ɼϊΠζͱΈͳ͠ɼϞσϧֶशͷલஈͰআ‫͢ڈ‬Δɽ͜ΕʹΑΓɼF0 ύλʔϯͷ֒‫ܗ‬ΛΑΓਫ਼౓ྑ͘ϞσϧԽ͢ΔΑ͏ʹ GMM ֶ͕श͞ΕΔͱ‫ظ‬଴͞ΕΔɽͳ͓ɼআ‫ॲڈ‬ཧʹ͸ɼ௿Ҭ௨աϑΟϧλʢLow-pass ﬁlter; LPFʣΛ༻͍Δɽ
4. 3. 3 U/V ༧ଌͷճආࣗવͳ F0 ύλʔϯΛੜ੒͢ΔͨΊʹ͸ɼU/V ৘ใΛ༧ଌ͠
෇༩͢Δඞཁ͕͋Δɽ͔͠͠ͳ͕ΒɼϋΠϒϦουํࣜʹ͓͚
Δ U/V ༧ଌॲཧ͸ຊ࣭తʹࠔ೉ͳॲཧͰ͋Γɼগͳ͔Βͣਪఆ‫͕ࠩޡ‬ੜ͡Δɽ͜ͷਪఆ‫ࠩޡ‬͸ɼ‫ڧ‬ௐԻ੠ͷ඼࣭ྼԽΛҾ͖‫͢͜ى‬ཁҼͱͳΓಘΔɽಛʹɼ༗੠ԻΛແ੠Իͱ͢Δ༧ଌ‫ࠩޡ‬ʢV to Uʣ͕‫ڧ‬ௐԻ੠ͷ඼࣭ʹ༩͑ΔӨ‫ڹ‬͸େ͖͍ɽEL ‫ڧ‬ௐॲཧʹ͓͍ͯɼ‫ڧ‬ௐલͷ EL Ի੠͸ɼԻ‫ݯ‬৴߸͕ੜ੒͞Ε͍ͯͳ͍ແԻ۠ؒΛআ͖ɼશͯ༗੠ԻͰ͋ΔɽͦͷͨΊɼແ੠۠ؒΛ࣋ͨͳ͍࿈ଓ F0 ύλʔϯΛ༻͍ͨͱͯ͠΋ɼ‫ڧ‬ௐલͱൺ΂ͯɼѱӨ‫ڹ‬͸ੜ͡ͳ͍ɽ‫ʹٯ‬ɼV to U ͷ༧ଌ‫ʹࠩޡ‬Α
Δ඼࣭ྼԽΛճආͰ͖Δͱ͍͏ར఺͕͋Δɽͦ͜ͰɼU/V ༧ଌΛߦΘͣɼ࿈ଓ F0 ύλʔϯΛ༻͍ͯ‫ڧ‬ௐԻ੠Λੜ੒͢Δɽͳ
͓ɼແԻ۠ؒʹؔͯ͠͸ɼEL Ի੠ͷ೾‫ܗ‬ύϫʔΛ༻͍ͯࣗಈ
తʹ‫ݕ‬ग़͠ɼແ੠ϑϨʔϜͱͯ͠߹੒͢Δɽ




ਤ6

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U/V ༧ଌॲཧͷ༗ແʹର͢Δ U/V ༧ଌ‫ࠩޡ‬

5. 2 ٬‫؍‬ධՁ࣮‫݁ݧ‬Ռ
ਤ 5 ʹԻ‫ݯ‬ಛ௃ྔ༧ଌ࣌ʹ͓͚Δ֤ख๏ʹ͓͚Δ F0 ਪఆਫ਼౓Λࣔ͢ɽCF0 ‫ ͼٴ‬LPF ͷಋೖʹΑΓ૬ؔ܎਺͕վળ͢Δɽ͜ΕΑΓɼֶशσʔλதͷ F0 ύλʔϯʹରͯ͠ɼແ੠۠ؒΛิؒ͠ɼϚΠΫϩϓϩισΟΛআ‫͢ڈ‬Δ͜ͱ͸༗ޮͰ͋Δͱ‫ݴ‬͑Δɽ·ͨɼ࠷దͳࠞ߹਺͸ 32 Ͱ͋Δɽ
ਤ 6 ʹԻ‫ݯ‬ಛ௃ྔ༧ଌ࣌ʹ͓͚Δ U/V ༧ଌॲཧͷ༗ແʹର͢Δ U/V ༧ଌ‫ࠩޡ‬Λࣔ͢ɽU/V ༧ଌॲཧͷճආʹΑΓɼV to
U ͷ༧ଌ‫ࠩޡ‬͸ 0 ͱͳΔ͕ɼU to V ͷ༧ଌ‫ࠩޡ‬͸૿େ͢Δɽͳ
͓ɼEL Ի੠΋ಉ༷ͷ༧ଌ‫ࠩޡ‬Λ࣋ͭͱߟ͑ΒΕΔɽ
5. 3 ओ‫؍‬ධՁ࣮‫݁ݧ‬Ռ
ਤ 7 ʹࣗવੑʹؔ͢Δ MOS ධՁ݁ՌΛࣔ͢ɽSS ʹΑΔࣗવੑվળޮՌ͸‫ۃ‬Ίͯখ͍͞ɽҰํͰɼVC ͸ɼஶ͍ࣗ͠વੑ
վળΛ΋ͨΒ͢ɽఏҊ๏͸ɼVC ʹ͸‫ٴ‬͹ͳ͍·Ͱ΋ɼి‫ؾ‬Ի੠ͷࣗવੑΛେ෯ʹվળͰ͖Δ͜ͱ͕෼͔Δɽ·ͨɼCF0 ͷಋೖʹΑΓɼࣗવੑ͕ए‫ׯ‬վળ͢Δ܏޲͕‫ݟ‬ΒΕΔɽ
ਤ 8 ʹॻ͖औΓࢼ‫݁ݧ‬ՌΛࣔ͢ɽจ‫[ ݙ‬5] ʹ͓͍ͯɼVC ʹ‫ ͮ͘ج‬EL Ի੠‫ڧ‬ௐ͸໌ྎੑΛྼԽͤ͞Δ͜ͱʢ୯‫ޠ‬ਖ਼ղ཰Ͱ໿ 3%ྼԽʣ͕ใࠂ͞Ε͍ͯΔ͕ɼϋΠϒϦουํࣜ͸໌ྎੑྼԽΛ΋ͨΒ͞ͳ͍͜ͱ͕෼͔Δɽ·ͨɼϋΠϒϦουํࣜʹ
͓͍ͯɼU/V ༧ଌΛճආͨ͠ࡍʹ͓͍ͯ΋ɼཧ૝తͳ U/V ৘ใΛ༻͍ͨ৔߹ͱಉ౳ͷ໌ྎੑ͕ಘΒΕ͍ͯΔ͜ͱ͔Βɼඞͣ͠΋ U/V ༧ଌ͕ඞཁͰ͸ͳ͍͜ͱ͕෼͔ΔɽҰํͰɼSS ͱൺ
ֱ͢Δͱɼ໌ྎੑ͕ए‫ׯ‬௿Լ͢Δ܏޲͕‫ݟ‬ΒΕΔɽ͜ͷ‫ݪ‬Ҽͱͯ͠ɼϘίʔμʹΑΔ೾‫߹ܗ‬੒ͷӨ‫͑ߟ͕ڹ‬ΒΕΔɽ
ਤ 9 ʹϓϦϑΝϨϯεςετͷ݁ՌΛࣔ͢ɽཧ૝తͳ U/V৘ใΛ༻͍ͨ৔߹ͱ U/V ༧ଌॲཧΛճආͨ͠৔߹ͱͰ༗ҙ͕ࠩ‫ݟ‬ΒΕͳ͍͜ͱ͔ΒɼU/V ༧ଌॲཧͷճආ͕ɼϋΠϒϦου
ͳख๏ʹ‫ڧͮ͘ج‬ௐԻ੠ͷࣗવੑʹ༩͑ΔӨ‫ڹ‬͸ͳ͍͜ͱ͕Θ
͔Δɽͳ͓ɼਤ 8 ಉ༷ʹɼཧ૝తͳ U/V ৘ใΛ༻͍ͨ৔߹ͱ
ͦͷଞͷ৔߹ͱൺֱͨ͠ࡍʹ༗ҙ͕ࠩ‫ݟ‬ΒΕͳ͍‫ݪ‬Ҽͱͯ͠͸ɼϘίʔμʹΑΔ೾‫߹ܗ‬੒ͷࡍͷεϖΫτϧಛ௃ྔͱ U/V ৘ใͷෆҰக͕ߟ͑ΒΕΔɽి‫ؾ‬Ի੠ͷൃ࿩த͸ඞͣ༗੠ԻͱͳΔ
ͨΊɼSS Λ༻͍ͯి‫ؾ‬Ի੠͔Βநग़͞ΕΔεϖΫτϧಛ௃ྔ͸શͯ༗੠ԻͷεϖΫτϧಛ௃ྔͱͳΔɽҰํͰɼ௨ৗԻ੠ͷ—5—   !  "# $%

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ࣗવੑʹؔ͢Δ MOS ධՁ݁Ռ   !    

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ਤ 9 ࣗવੑʹؔ͢ΔϓϦϑΝϨϯεςετ݁Ռ৔߹ɼ༗੠Իͱແ੠ԻͰɼεϖΫτϧಛ௃ྔʢྫ͑͹εϖΫτ
ϧ܏ࣼʣ͸େ͖͘มԽ͢Δɽ‫ݱ‬ঢ়ͷϋΠϒϦου๏Ͱ͸ɼU/V৘ใʹ‫ͮ͘ج‬εϖΫτϧಛ௃ྔͷิਖ਼ॲཧΛߦ͍ͬͯͳ͍ͷͰɼ༗੠ԻͷεϖΫτϧಛ௃ྔͱແ੠ԻͷԻ‫ݯ‬৴߸͕৞Έࠐ·ΕΔ
৔߹͕͋ΔɽͦͷͨΊɼཧ૝తͳ U/V ৘ใΛԻ‫ݯ‬ಛ௃ྔʹ൓өͤ͞Δ͚ͩͰ͸ɼԻ࣭վળʹͭͳ͕Βͳ͍ͱߟ͑ΒΕΔɽ

6. ऴ Θ Γ ʹຊใࠂͰ͸ɼ໌ྎੑͷྼԽΛҾ͖‫ࣗʹͣ͜͞ى‬વੑΛେ෯ʹվળ͢Δి‫ؾ‬Ի੠‫ڧ‬ௐ๏ͱͯ͠ɼզʑ͕ఏҊͨ͠ࡶԻ཈ѹʹ‫ج‬ͮ͘εϖΫτϧิਖ਼ॲཧͱ౷‫ܭ‬త੠࣭ม‫ͮ͘جʹ׵‬Ի‫ݯ‬ಛ௃ྔੜ੒ॲཧΛ૊Έ߹ΘͤͨϋΠϒϦου๏ʹ͍ͭͯड़΂ͨɽ٬‫؍‬ධՁ࣮‫ݧ‬ͷ݁Ռ͔Βɼ࿈ଓ F0 ϞσϧͷಋೖɼϚΠΫϩϓϩι
σΟͷআ‫ڈ‬ͷ༗ޮੑΛࣔͨ͠ɽ·ͨɼओ‫؍‬ධՁ࣮‫ݧ‬ͷ݁Ռ͔ΒɼఏҊ๏ͷ༗ޮੑΛࣔ͠ɼి‫ؾ‬Ի੠‫ڧ‬ௐʹ͓͍ͯ༗੠ແ੠৘ใͷ༧ଌॲཧΛճආͰ͖Δ͜ͱΛࣔͨ͠ɽँࣙɿຊ‫ڀݚ‬ͷҰ෦͸ɼJSPS Պ‫ݚ‬අ 22680016 ͷॿ੒Λड͚࣮ࢪͨ͠΋ͷͰ͋Δɽจ‫ݙ‬[2] Y. Stylianou, O. Cappe, and E. Moulines, “Continuous probabilistic transform for voice conversion,” Speech
and Audio Processing, IEEE Transactions on, vol.6, no.2,
pp.131–142, March 1998.
[3] T. Toda, A.W. Black, and K. Tokuda, “Voice conversion
based on maximum-likelihood estimation of spectral parameter trajectory,” Audio, Speech, and Language Processing,IEEE Transactions on, vol.15, no.8, pp.2222–2235, Nov.
2007.
[4] K. Nakamura, T. Toda, H. Saruwatari, and K. Shikano,“Speaking-aid systems using gmm-based voice conversion for electrolaryngeal speech,” SPECOM, vol.54, no.1,
pp.134–146, Jan. 2012.
[5] H. Doi, T. Toda, K. Nakamura, H. Saruwatari, and K.
Shikano, “Alaryngeal speech enhancement based on oneto-many eigenvoice conversion,” Audio, Speech, and Language Processing, IEEE/ACM Transactions on, vol.22,
no.1, pp.172–183, Jan. 2014.
[6] H.R. Sharifzadeh, I.V. McLoughlin, and F. Ahmadi, “Reconstruction of normal sounding speech for laryngectomy
patients through a modiﬁed CELP codec,” Biomedical Engineering, IEEE Transactions on, vol.57, no.10, pp.2448–
2458, Oct. 2010.
[7] C.Y. Espy-Wilson, V.R. Chari, J.M. MacAuslan, C.B.
Huang, and M.J. Walsh, “Enhancement of electrolaryngeal
speech by adaptive ﬁltering,” Journal of Speech, Language,and Hearing Research, vol.41, no.6, p.1253, Dec. 1998.
[8] S. Boll, “Suppression of acoustic noise in speech using spectral subtraction,” Acoustics, Speech and Signal Processing,IEEE Transactions on, vol.27, no.2, pp.113–120, April 1979.
[9] B.L. Sim, Y.C. Tong, J.S. Chang, and C.T. Tan, “A parametric formulation of the generalized spectral subtraction
method,” Speech and Audio Processing, IEEE Transactions
on, vol.6, no.4, pp.328–337, July 1998.
[10] H. Kawahara, J. Estill, and O. Fujimura, “Aperiodicity extraction and control using mixed mode excitation and groupdelay manipulation for a high quality speech analysis, modiﬁcation and synthesis system straight,” Proc. MAVEBA,
pp.13–15, Sept. 2001.
[11] Y. Ohtani, T. Toda, H. Saruwatari, and K. Shikano, “Maximum likelihood voice conversion based on GMM withSTRAIGHT mixed excitation,” Proc. INTERSPEECH,
pp.2266–2269, Sept. 2006.
[12] A. Kain and M.W. Macon, “Spectral voice conversion for
text-to-speech synthesis,” Acoustics, Speech and Signal
Processing, 1998. Proceedings of the 1998 IEEE International Conference on, vol.1, pp.285–288, May 1998.
[13] T. Toda, M. Nakagiri, and K. Shikano, “Statistical voice
conversion techniques for body-conducted unvoiced speechenhancement,” Audio, Speech, and Language Processing,IEEE Transactions on, vol.20, no.9, pp.2505–2517, Nov.
2012.
[14] K. Yu and S. Young, “Continuous F0 modeling for
HMM based statistical parametric speech synthesis,” Audio, Speech, and Language Processing, IEEE Transactions
on, vol.19, no.5, pp.1071–1079, July 2011.
[15] H. Kawahara, I. Masuda-Katsuse, and A. deCheveigné,“Restructuring speech representations using a pitchadaptive time–frequency smoothing and an instantaneousfrequency-based F0 extraction: Possible role of a repetitive
structure in sounds,” SPECOM, vol.27, no.3, pp.187–207,
April 1999.

[1] H. Liu, Q. Zhao, M. Wan, and S. Wang, “Enhancement of electrolarynx speech based on auditory masking,”
Biomedical Engineering, IEEE Transactions on, vol.53,
no.5, pp.865–874, May 2006.

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