୍⯡♫ᅋἲே 㟁Ꮚ᝟ሗ㏻ಙᏛ఍                      ಙᏛᢏሗ‫ٻ‬‫ٻڇڮڞڤکڪڭگڞڠڧڠٻڡڪٻڠگڰگڤگڮکڤٻڠڣگ‬                     ৴ֶٕใ‫ٻۏۍۊۋۀڭٻۇڼھۄۉۃھۀگٻڠڞڤڠڤ‬ࣾஂ๏ਓ ిࢠ৘ใ௨৴ֶձ
‫ٻڮڭڠڠکڤڢکڠٻکڪڤگڜڞڤکڰڨڨڪڞٻڟکڜٻکڪڤگڜڨڭڪڡکڤ‬                TECHNICAL‫ٻڄڍڌڈڏڌڋڍڃڐڌڌڈڏڌڋڍګڮ‬THE INSTITUTE OF ELECTRONICS,REPORT OF IEICE.
‫ٻ‬INFORMATION AND COMMUNICATION ENGINEERS

Ի੠ೖྗʹ‫ͮ͘ج‬ӆ཯੍‫ػޚ‬ೳΛ༗͢Δ HMM Ի੠߹੒γεςϜ੢֞ ༑ཧ†

ߴಓ৻೭հ†

‫ాށ‬

ஐ‫†ج‬

Graham Neubig†

Sakriani Sakti†தଜɹ఩†† ಸྑઌ୺Պֶٕज़େֶӃେֶ͋Β·͠˟ 630–0192 ಸྑ‫ݝ‬ੜۨࢢߴࢁொ 8916–5Ի੠߹੒Λ༻͍ͨ૑࡞‫׆‬ಈͷ‫ൃ׆‬ԽʹΑΓɼ໨ඪͱ͢Δಛఆ࿩ऀͷԻ੠ͷ߹੒ॲཧʹ͓͍ͯɼϢʔβͷࢥ͍௨ΓͷԻ੠Λ߹੒Ͱ͖ΔΠϯλʔϑΣʔεͷ։ൃ͕๬·Ε͍ͯΔɽ͜Εʹରͯ͠ຊใࠂͰ͸ɼߴ඼࣭͔ͭද‫ྗݱ‬๛͔ͳԻ੠߹੒ͷ࣮‫ݱ‬Λ໨ࢦͯ͠ɼ HMM Ի੠߹੒ʹ͓͍ͯɼ௨ৗͷςΩετԻ੠߹੒‫ػ‬ೳΛอ࣋ͭͭ͠ɼϢʔβʹΑ
ΔೖྗԻ੠Λ༻͍ͯ໨ඪ࿩ऀͷ߹੒Ի੠ͷӆ཯Λ੍‫͢ޚ‬Δख๏ΛఏҊ͢ΔɽೖྗԻ੠ʹରͯ͠ɼ HMM ঢ়ଶΞϥΠϝϯτΛߦ͏͜ͱͰɼೖྗԻ੠ͷ‫ܧ‬ଓ௕Λநग़͠ɼͦΕΛ໨ඪ࿩ऀ༻ HMM ʹ൓өͤ͞Δ͜ͱͰɼೖྗԻ੠ͷ‫ܧ‬ଓ௕ʹରԠͨ͠໨ඪ࿩ऀͷ߹੒Ի੠Λੜ੒͢Δɽ͞ΒʹɼೖྗԻ੠ͷ F0 ύλʔϯΛ߹੒Ի੠ʹ൓өͤ͞Δ͜ͱͰɼೖྗԻ੠
ͷ F0 ύλʔϯ͓Αͼ‫ܧ‬ଓ௕ʹରԠͨ͠໨ඪ࿩ऀͷ߹੒Ի੠Λ࣮‫͢ݱ‬ΔɽೖྗԻ੠ͱ߹੒Ի੠ؒʹ͓͚Δӆ཯ύϥϝʔλͷෆҰகʹΑΔࣗવੑྼԽΛ཈͑ΔͨΊʹɼ൓өͤ͞Δ‫ܧ‬ଓ௕ͷ୯Ґʹؔ͢Δ‫ݕ‬౼ɼϞσϧదԠॲཧΛ༻͍ͨΞϥΠϝϯτ༻ HMM ͷߏஙʹؔ͢Δ‫ݕ‬౼ɼ͓Αͼɼ༗੠ / ແ੠৘ใʹରԠ͢Δิਖ਼ॲཧʹؔ͢Δ‫ݕ‬౼Λߦ͏ɽෳ਺ͷೖྗ࿩ऀΛର৅ͱ࣮ͨ͠‫ݧ‬తධՁ݁Ռ͔ΒɼఏҊ๏ͷ༗ޮੑΛࣔ͢ɽΩʔϫʔυ

HMM Ի੠߹੒ɼӆ཯੍‫ޚ‬ɼ‫ܧ‬ଓ௕୯ҐɼϞσϧదԠɼ༗੠ / ແ੠৘ใHMM-Based Speech Synthesis System with Prosody Modiﬁcation Basedon Speech Input
Yuri NISHIGAKI† , Shinnosuke TAKAMICHI† , Tomoki TODA† , Graham NEUBIG† , Sakriani
SAKTI† , and Satoshi NAKAMURA†† Nara Institute of Science and Technology, Tatayama-cho 8916–5, Ikoma, Nara, 630–0192 Japan
Abstract As a creative activity using speech synthesis technologies has been grown rapidly, it is desired to develop
an interface to synthesize speech of a speciﬁc target speaker as users want. In this report, we propose a prosody
modiﬁcation method using user’s speech inputs in HMM-based speech synthesis system in order to achieve high-quality and expressive speech synthesis. The propose method allows users to guide prosody of synthetic speechof the target speaker by using their own voices while preserving original functions of the HMM-based speech synthesis system as a text-to-speech synthesis system. Both duration information of the input speech extracted by
performing HMM state alignment and F0 patterns of the input speech are eﬀectively used to control the duration
and F0 patterns of synthetic speech of the target speaker. To alleviate the degradation of naturalness caused byprosodic mismatches between the input speech and the synthetic speech, we investigate an appropriate unit for
the HMM state alignment, model adaptation for building an HMM used for the alignment, and correction of unvoiced/voiced information. Experimental evaluations are conducted for multiple input speakers, which demonstrates
the eﬀectiveness of the proposed method.
Key words HMM-based speech synthesis, prosody modiﬁcation, alignment unit, model adaptation, unvoiced/voiced information—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

1. · ͑ ͕ ͖Ի੠ͷ‫ܧ‬ଓ௕ʹରԠͨ͠໨ඪ࿩ऀͷ߹੒Ի੠Λੜ੒͢Δɽ͞ΒʹɼೖྗԻ੠ͷ F0 ύλʔϯΛ໨ඪ࿩ऀͷ߹੒Ի੠΁ͱసࣸ͢ίʔύεϕʔεԻ੠߹੒ٕज़ʹΑΓɼ߹੒Ի੠ͷ໌ྎੑ͓ΑͼΔɽຊॲཧΛߦ͏্ͰɼϢʔβʹΑΔೖྗԻ੠ͱ໨ඪԻ੠ͱͷࣗવੑͷվળͷΈͰͳ͘ɼ࿩ऀੑ ( ‫ݸ‬ਓੑ ) ͷ࠶‫ݱ‬ਫ਼౓ʹؔͯؒ͠ʹ͸ɼӆ཯ύϥϝʔλͷෆҰக͕ੜ͡ΔͨΊɼ߹੒Ի੠ͷ඼΋ɼඈ༂తͳվળ͕΋ͨΒ͞Εͨ [1] ɽ͜Εʹ൐͍ɼςΩετԻ੠࣭͕ྼԽ͢ΔՄೳੑ͕͋Δɽ͜ͷ໰୊ʹରԠ͢ΔͨΊɼ൓ө͞߹੒γεςϜ CeVIO [2] ΍Վ੠߹੒γεςϜ VOCALOID [3]ͤΔ‫ܧ‬ଓ௕ͷ୯ҐͷબఆɼϞσϧదԠॲཧΛ༻͍ͨΞϥΠϝϯ

ʹ୅ද͞ΕΔΑ͏ʹɼಛఆͷΩϟϥΫλੑΛ༗͢ΔԻ੠ / Վ੠τ༻ HMM ͷੑೳվળɼ͓Αͼɼ༗੠ / ແ੠৘ใͷෆҰகʹରͷ߹੒ٕज़ʹର͢Δधཁ͕೔ʑ૿͓ͯ͠Γɼॴ๬ͷԻ੠ / Վ੠Ԡ͢Δิਖ਼ॲཧʹؔ͢Δ‫ݕ‬౼Λߦ͏ɽ‫ݸ‬ʑͷॲཧͷޮՌΛධՁΛ૑࡞͢Δ‫׆‬ಈʹ͓͍ͯɼͦͷར༻͕େ͍ʹ‫ظ‬଴͞Ε͍ͯΔɽ

͢ΔͨΊʹɼෳ਺ͷೖྗ࿩ऀΛର৅ͱ࣮ͨ͠‫ݧ‬తධՁΛߦ͏ɽԻ੠૑࡞‫׆‬ಈʹ͓͍ͯ͸ɼςΩετ͔ΒԻ੠Λ߹੒͢ΔςΩετԻ੠߹੒ٕज़͸͔ܽͤͳ͍‫ج‬൫ٕज़Ͱ͋ΔɽͦͷதͰ΋ɼ

2.

HMM Ի੠߹੒

HMM Ի੠߹੒ [4] ɼ[5] ͸ɼ߹੒Ի੠ͷ੠࣭΍ӆ཯తಛ௃Λॊ

HMM Ի੠߹੒ʹ͓͚Δֶश෦Ͱ͸ɼࣗવԻ੠ͷύϥϝʔೈʹ੍‫͢ޚ‬Δ͜ͱ͕ՄೳͰ͋ΔͨΊ ɼͦͷ༗ޮੑ͸‫ۃ‬Ίͯߴλ ( ε ϖ Ϋ τ ϧ ɼF0 ɼඇ प ‫ ظ‬੒ ෼ ) ‫ ྻ ܥ‬Λ ί ϯ ς Ω ε τ ґ͍ɽԻ੠ύϥϝʔλΛ௚઀खಈͰૢ࡞͢ΔγεςϜ [6] ͱൺֱଘ HMM ʹΑΓϞσϧԽ͢Δɽ·ͨɼঢ়ଶ‫ܧ‬ଓ௕Ϟσϧͷಋೖ͠ɼந৅Խ͞ΕͨϞσϧύϥϝʔλͷखಈૢ࡞ʹΑΔ߹੒Ի੠ʹΑΓɼ֤छԻ੠ύϥϝʔλͱ‫ܧ‬ଓ௕Λ౷Ұతͳ࿮૊ΈͷԼͰϞͷ੍‫͕ޚ‬ՄೳͰ͋ΔͨΊ [7] ɼΑΓߴ͍ૢ࡞ੑ͕ಘΒΕΔɽҰσϧԽ͢Δ͜ͱ͕ՄೳͰ͋Δ [5] ɽੜ੒࣌ʹ͸ɼೖྗςΩετΛ

ํͰɼϢʔβͷࢥ͍௨ΓͷԻ੠Λ߹੒͢Δ͜ͱ͸ະͩ༰қͰ͸ղੳ͢Δ͜ͱͰಘΒΕΔίϯςΩετʹ‫͖ͮج‬ɼจ HMM Λߏͳ͘ɼΑΓ࢖͍উखͷྑ͍ϢʔβΠϯλϑΣʔεͷߏங͕๬·ங͠ɼঢ়ଶ‫ܧ‬ଓ௕Ϟσϧ͔Β HMM ঢ়ଶ‫ ྻܥ‬q = [q1 , · · · , qT ] ΛΕΔɽ࣍ࣜʹΑΓܾఆ͢ΔɽϢʔβ͕ҙਤͨ͠߹੒Ի੠Λ࡞੒͢Δ্Ͱɼखಈૢ࡞Λิ‫׬‬͢Δೖྗ৘ใͱͯ͠ɼϢʔβ͕ൃ੠͢ΔԻ੠৘ใΛ༻͍Δ࿮૊Έ͕ߟ͑ΒΕΔɽྫ͑͹ɼՎ੠߹੒ͷ෼໺ʹ͓͍ͯ͸ɼॴ๬ͷද৘Λ࣋ͭՎ੠Λ߹੒͢ΔͨΊʹɼϢʔβʹΑΔՎ੠Λࢀরσʔλͱͯ͠ೖྗ͠ɼVOCALOID ͷೖྗύϥϝʔλΛࣗಈͰ࠷దԽ͢Δख๏͕ఏҊ͞Ε͍ͯΔ [8] ɼ[9] ɽϢʔβ͕‫ٻ‬ΊΔද৘Λࣗ਎ͷՎ੠ʹΑΓද‫͢ݱ‬Δ͜ͱͰɼ VOCALOID Ͱ߹੒͞ΕΔಛఆՎखͷՎ੠ʹ൓өͤ͞Δ͜ͱ͕Ͱ͖ɼΑΓ௚‫ײ‬త͔ͭ༰қʹॴ๬ͷՎ੠͕߹੒ՄೳͱͳΔɽ࿩͠੠Λର৅ͱͨ͠Ի੠

q̂ = argmax P (q|λ)q(1)ͨͩ͠ɼT ͸ϑϨʔϜ਺ɼqt ͸ϑϨʔϜ t ʹ͓͚Δ HMM ঢ়ଶΠϯσοΫεɼλ ͸ HMM ͷύϥϝʔληοτͰ͋ΔɽԻ੠ύϥϝʔλ‫ྻܥ‬͸ɼ੩తɾಈతಛ௃ྔؒͷ໌ࣔతͳ੍໿ͷԼͰɼ

HMM ໬౓Λ࠷େԽ͢ΔΑ͏ʹੜ੒͞ΕΔ [13] ɽ
c = argmax P (W c|q̂, λ)
c



(2)



߹੒ͷ෼໺ʹ͓͍ͯ΋ɼྨࣅͨ͠࿮૊Έͱͯ͠ɼϢʔβͷೖྗͨͩ͠ɼc = c
1 , · · · , ct , · · · , cTԻ੠ͷ੠࣭Λॴ๬ͷ໨ඪ࿩ऀͷ੠࣭΁ͱม‫͢׵‬Δ੠࣭ม‫ٕ׵‬

ct = [c (1) , · · · , c (D)] ͸ϑϨʔϜ t ʹ͓͚ΔԻ੠ύϥϝʔλज़ [10] ɼ[11] ͷར༻͕ߟ͑ΒΕΔɽೖྗԻ੠ͷӆ཯ಛ௃Λอ࣋ϕΫτϧɼW ͸ಈతಛ௃ྔͷ‫͍༻ʹࢉܭ‬ΔॏΈʹΑͬͯߏ੒͞

͢Δ͜ͱ͕ՄೳͰ͋ΔͨΊɼॴ๬ͷӆ཯ʹΑΔ໨ඪ࿩ऀͷԻ੠

ΕΔߦྻ [4] Ͱ͋Δɽग़ྗԻ੠͸ɼੜ੒ύϥϝʔλ͔ΒϘίʔ͕࡞੒ՄೳͱͳΔɽ͔͠͠ͳ͕Βɼ੠࣭ม‫׵‬͸ HMM Ի੠߹੒μʔʹ‫ͮ͘ج‬೾‫ܗ‬ੜ੒ॲཧΛ‫߹ͯܦ‬੒͞ΕΔɽHMM Ի੠߹੒ͱൺֱͯ͠ɼ߹੒Ի੠ͷ඼࣭͓Αͼ࿩ऀੑ࠶‫ݱ‬ਫ਼౓͕ྼԽ͢Δ

ͷ߹੒Ի੠ͷԻ࣭͸ɼࣗવԻ੠ͱൺֱͯ͠ྼԽ͢Δ܏޲ʹ͋Δ

܏޲ʹ͋Δɽ͜Εʹରͯ͠ɼHMM Ի੠߹੒ͷ࿮૊Έʹ͓͍ͯɼ͕ɼύϥϝʔλੜ੒࣌ʹ‫಺ྻܥ‬มಈΛߟྀ͢Δ͜ͱͰɼԻ࣭ͷೖྗ͞ΕͨԻ੠ͷӆ཯ಛ௃Λ߹੒Ի੠ʹ൓өͤ͞Δ࿮૊Έ͕ఏվળ͕ՄೳͰ͋Δ [14]ɽ

Ҋ͞Ε͍ͯΔ [12] ɽ͜ͷ࿮૊ΈͰ͸ɼHMM Ի੠߹੒ʹΑΔ඼
࣭Λอ࣋ͨ͠··ɼೖྗԻ੠ʹΑΔӆ཯ૢ࡞͕ՄೳͱͳΔɽҰ͸Ի੠ύϥϝʔλ‫ྻܥ‬ɼ

3. Ի੠ʹΑΔӆ཯੍‫ޚ‬Λ༗͢Δ HMM Ի੠߹੒ํͰɼೖྗԻ੠ͷӆ཯Λ൓өͤ͞Δ্ͰಠࣗͷίϯςΩετΛ

໨ඪ࿩ऀͷ HMM Λ༻͍ͯɼϢʔβ͕ೖྗͨ͠Ի੠ͷӆ཯Λ༻͍Δඞཁ͕͋ΔͨΊɼैདྷͷςΩετԻ੠߹੒γεςϜͱ͠໛฿ͨ͠߹੒Ի੠Λੜ੒͢ΔͨΊʹɼԻ੠ʹΑΔӆ཯੍‫ޚ‬๏ΛͯͷੑೳΛอ࣋Ͱ͖Δอূ͸ͳ͍ɽԾʹɼςΩετԻ੠߹੒γఏҊ͢ΔɽఏҊ๏ʹ͓͚ΔॲཧͷྲྀΕΛਤ 1 ʹࣔ͢ɽೖྗςΩεςϜͱͯ͠ͷ‫ػ‬ೳΛอ࣋ͭͭ͠ɼೖྗԻ੠ʹΑΔӆ཯੍‫ޚ‬Λετ͓ΑͼೖྗԻ੠Λ༻͍ͯɼॴ๬ͷӆ཯Λ࣋ͭ໨ඪ࿩ऀͷԻՄೳͱ͢Δ࿮૊ΈΛ࣮‫͢ݱ‬Δ͜ͱ͕Ͱ͖Ε͹ɼखಈૢ࡞ͱೖྗ

੠Λ߹੒͢Δɽͳ͓ɼ໨ඪ࿩ऀͷ HMM ͸ςΩετԻ੠߹੒Ͱ

Ի੠ʹΑΔૢ࡞Λ૬ิతʹ࢖༻͢Δ͜ͱ΋ՄೳͱͳΓɼΑΓૢ༻͍ΒΕΔ΋ͷͱಉҰͰ͋ΔͨΊɼԻ੠͕ೖྗ͞Εͳ͍ࡍʹ͸ɼ࡞ੑʹ༏ΕͨΠϯλϑΣʔεΛ࣮‫͖Ͱݱ‬Δͱߟ͑ΒΕΔɽ

௨ৗͷ HMM Ի੠߹੒ॲཧʹΑΓԻ੠Λ߹੒Ͱ͖Δɽ

ຊใࠂͰ͸ɼ HMM Ի੠߹੒ʹ͓͍ͯɼ௨ৗͷςΩετԻ੠

3. 1 ‫ܧ‬ଓ௕ͷ੍‫ޚ‬߹੒‫ػ‬ೳΛอ࣋͠ɼ͔ͭɼԻ੠Λ༻͍ͯ߹੒Ի੠ͷӆ཯੍‫ޚ‬ΛೖྗԻ੠ͷ‫ܧ‬ଓ௕৘ใΛநग़͢ΔͨΊʹɼHMM ঢ়ଶΞϥΠ

Մೳͱ͢Δख๏ΛఏҊ͢ΔɽೖྗԻ੠ʹରͯ͠ɼHMM ঢ়ଶΞϝϯτΛߦ͏ɽ·ͣɼೖྗԻ੠ͷԻ‫ڹ‬ಛ௃ྔʹରԠͨ͠ΞϥΠϥΠϝϯτΛߦ͏͜ͱͰɼೖྗԻ੠ͷ‫ܧ‬ଓ௕Λநग़͢ΔɽͦΕϝϯτ༻ͷ HMM Λ༻ҙ͠ɼೖྗςΩετʹԠͨ͡จ HMM ΛΛ߹੒࣌ʹ༻͍Δ໨ඪ࿩ऀ༻ HMM ʹ൓өͤ͞Δ͜ͱͰɼೖྗ

ߏ੒͢Δɽ࣍ʹɼೖྗԻ੠ʹରͯ͠ HMM ঢ়ଶΞϥΠϝϯτΛ

—2—

Input speech

Input text

Parameter extraction
Spectrum, F0
F0& aperiodicity

Text analysis
Context labels
HMMs for alignment

Forced alignment
DurationTarget speakerʼs HMM
Parameter generation
F0
F0Spectrum & aperiodicity

F0 modif ication

U/V compensation
F0
Waveform generation
Synthetic speechਤ 1 Ի੠ʹΑΔӆ཯੍‫ॲޚ‬ཧͷϒϩοΫਤ

Fig. 1 Diagram of prosody modiﬁcation based on speech inputs.

४ʹΑΓܾఆ͢Δ [5]ɽ3. 2 F0 ‫ྻܥ‬ͷ੍‫ޚ‬ೖྗԻ੠ͷ‫ܧ‬ଓ௕ʹ‫͖ͮج‬ੜ੒͞Εͨ߹੒Ի੠ύϥϝʔλʹରͯ͠ɼ͞ΒʹೖྗԻ੠ͷ F0 ‫ྻܥ‬Λ൓өͤ͞Δɽ࠷ऴతʹಘΒΕͨ߹੒Ի੠ύϥϝʔλ͔ΒɼೖྗԻ੠ͷ‫ܧ‬ଓ௕͓Αͼ F0 ‫ܥ‬ྻΛ࣋ͭ໨ඪ࿩ऀͷ߹੒Ի੠Λੜ੒͢Δɽ

3. 2. 1 F0 ‫ྻܥ‬ͷੜ੒ͱԻߴม‫׵‬߹੒Ի੠ͷ F0 ‫ྻܥ‬ΛɼೖྗԻ੠ͷ F0 ‫ͱྻܥ‬ೖΕସ͑Δ͜ͱ
ͰɼೖྗԻ੠ͷ‫ܧ‬ଓ௕͓Αͼ F0 ‫ྻܥ‬Λ࣋ͭ߹੒Ի੠ύϥϝʔλ‫ྻܥ‬Λߏங͢Δɽͦͷࡍʹɼೖྗ࿩ऀͱ໨ඪ࿩ऀͷ F0 ൣғͷࠩΛิਖ਼͢ΔͨΊʹɼೖྗԻ੠ͷ F0 ʹରͯ͠ɼҎԼͷઢ‫ܗ‬ม‫׵‬Λߦ͏ɽxˆt =σy(xt − μx ) + μy
σx

(3)

ͨͩ͠ xt ͸ϑϨʔϜ t ʹ͓͚ΔೖྗԻ੠ͷର਺ F0 ɼμx ͱ σx ͸ߦ͏͜ͱͰɼೖྗԻ੠ͷঢ়ଶ‫ܧ‬ଓ௕Λܾఆ͢Δɽ߹੒Ի੠ͷੜͦΕͧΕ xt ͷฏ‫ͱۉ‬ඪ४ภࠩɼμy ͱ σy ͸߹੒͢Δ໨ඪ࿩ऀͷ੒ʹ༻͍Δ໨ඪ࿩ऀ HMM Λ༻͍ͯɼೖྗςΩετʹԠͨ͡

ର਺ F0 ͷฏ‫ͱۉ‬ඪ४ภࠩͰ͋ΔɽಘΒΕͨ߹੒Ի੠ύϥϝʔจ HMM Λߏ੒͠ɼೖྗԻ੠͔ΒಘΒΕͨ‫ܧ‬ଓ௕ͷ৘ใʹ‫ͮج‬λ͔ΒɼೖྗԻ੠ͷ‫ܧ‬ଓ௕͓Αͼ F0 ‫ྻܥ‬Λ࣋ͭ໨ඪ࿩ऀͷ߹

͖ɼHMM ঢ়ଶ‫ྻܥ‬Λܾఆ͢Δɽ࠷ऴతʹɼैདྷͷ࿮૊Έͱಉ੒Ի੠Λੜ੒͢Δɽ༷ʹɼ໬౓࠷େԽ‫ج‬४ʹ‫͖ͮج‬Ի੠ύϥϝʔλΛੜ੒͢Δ͜ͱ
ͰɼೖྗԻ੠ͷ‫ܧ‬ଓ௕Λ࣋ͭ໨ඪ࿩ऀͷ߹੒Ի੠ύϥϝʔλ‫ܥ‬ྻ͕ಘΒΕΔɽຊใࠂʹ͓͍ͯɼओʹҎԼͷ 2 ఺ʹ͍ͭͯ஫ҙਂ͘‫ݕ‬౼͢Δ
ඞཁ͕͋Δɽ

1ɿ ೖྗԻ੠ʹରͯ͠े෼ͳਫ਼౓Ͱ HMM ঢ়ଶΞϥΠϝϯτ
Λߦ͏ͨΊʹɼೖྗԻ੠ͷԻ‫ڹ‬ಛ௃ྔΛద੾ʹϞσϧԽ͍ͯ͠ΔΞϥΠϝϯτ༻ HMM Λ༻͍Δඞཁ͕͋Δɽ

3. 2. 2 ༗੠/ແ੠۠ؒͷิਖ਼ೖྗԻ੠ͷ F0 ‫ྻܥ‬͸ɼೖྗԻ੠ʹґଘͨ͠༗੠ / ແ੠৘ใΛ࣋ͭɽҰํͰɼ߹੒࣌ʹ࢖༻͢ΔεϖΫτϧύϥϝʔλ‫ྻܥ‬͸໨ඪԻ੠ʹରԠ͍ͯ͠ΔͨΊɼεϖΫτϧͱ༗੠ / ແ੠৘ใͷෆҰக͕ੜ͡ΔՄೳੑ͕͋Δɽͦ͜Ͱɼ໨ඪ࿩ऀ HMM ͔Βੜ੒͞ΕΔ F0 ‫ྻܥ‬ͷ༗੠/ແ੠৘ใΛ༻͍ͯɼೖྗԻ੠ͷ F0 ‫ܥ‬ྻͷ༗੠ / ແ੠৘ใΛิਖ਼͢Δɽ
·ͣɼೖྗԻ੠ͷ F0 ‫ʹྻܥ‬ରͯ͠εϓϥΠϯิؒॲཧΛߦ͏

2ɿ ΞϥΠϝϯτ༻ HMM ͱԻ੠߹੒༻ HMM ͸ҟͳΔ࿩

͜ͱͰɼແ੠۠ؒͷ F0 Λਪఆ͠ɼ࿈ଓతͳ F0 ‫ྻܥ‬ΛಘΔ [16]ऀͷԻ੠ΛϞσϧԽ͢Δ΋ͷͰ͋Γɼ྆ HMM ʹ͓͚Δ‫ݸ‬ʑɼ[17] ɽ͜ͷࡍʹɼϚΠΫϩϓϩισΟ [18] ͷআ‫ڈ‬΋ߦ͏ɽಘͷ HMM ঢ়ଶ͸ಉҰͷԻ‫ڹ‬ಛ௃ྔηάϝϯτΛϞσϧԽ͢Δ

ΒΕͨ࿈ଓతͳ F0 ‫ʹྻܥ‬ରͯ͠ɼ໨ඪ࿩ऀ HMM ʹΑΓܾఆอূ͸ͳ͍ ( ྫ͑͹ɼ͋ΔԻૉʹର͢Δ 5 ঢ়ଶ HMM ʹ͓͍ͯɼ͞ΕΔແ੠৘ใΛ෇༩͢Δ͜ͱͰɼ߹੒Ի੠ͷ F0 ‫ྻܥ‬Λੜ੒ΞϥΠϝϯτ༻ HMM Ͱ͸ 2 ঢ়ଶ໨ͰԻૉத৺෦ͷఆৗՕॴΛ͢ΔɽϞσϧԽ͢Δͷʹର͠ɼԻ੠߹੒༻ HMM Ͱ͸ 4 ঢ়ଶ໨ͰԻૉ

্‫ه‬ͷॲཧʹΑͬͯಘΒΕΔ F0 ‫ྻܥ‬ͷҰྫΛਤ 2 ʹࣔ͢ɽத৺෦ͷఆৗՕॴΛϞσϧԽ͢Δͱ͍͏͜ͱ͕‫͜ى‬ΓಘΔ ) ͨਤ 2 ΑΓɼೖྗԻ੠ͷ F0 ‫ྻܥ‬ͷԻߴ͓Αͼ༗੠ / ແ੠͕۠ؒิ

Ίɼ஌֮తʹѱӨ‫͕ڹ‬ੜ͡ͳ͍ൣғͰ‫ܧ‬ଓ௕Λ൓өͤ͞Δඞཁਖ਼͞Ε͍ͯΔ͜ͱ͕Θ͔Δɽ

͕͋Δɽ

1 ͭ໨ͷ఺ʹؔͯ͠͸ɼຊใࠂͰ͸ɼΞϥΠϝϯτ༻ HMM ͱ

4. ࣮‫ݧ‬తධՁͯ͠ɼ1 ) ೖྗ࿩ऀ ( Ի੠ೖྗΛߦ͏Ϣʔβ ) ͷԻ੠͕ࣄલʹे

4. 1 ࣮ ‫ ݧ‬৚ ݅෼ͳྔೖखͰ͖Δ৔߹Λ૝ఆͨ͠ೖྗ࿩ऀ HMM ͱɼ2 ) ೖྗ࿩໨ඪ࿩ऀ HMM ͷֶशσʔλͱͯ͠ɼ೔ຊਓঁੑ࿩ऀʹΑऀͷগྔͷԻ੠Λ༻͍ͯ໨ඪ࿩ऀ HMM ΛϞσϧదԠ [15] ͢

Δ ATR Իૉόϥϯεจ [19] தͷ A - I ηοτͷ‫ ܭ‬450 จΛ༻͍Δ͜ͱͰಘΒΕͨదԠ HMM ͷ࢖༻Λ‫ݕ‬౼͢Δɽͳ͓ɼϞσϧΔɽҰํͰɼԻ੠ೖྗΛߦ͏࿩ऀʢ ೖྗ࿩ऀ ʣ͸ɼ໨ඪ࿩ऀͱ

దԠͷࡍʹ͸ɼෆಛఆ࿩ऀϞσϧ΍ฏ‫ۉ‬੠Ϟσϧ [15] Λ༻͍Δ

͸ҟͳΔ೔ຊਓஉੑ / ঁੑ࿩ऀ֤ 2 ໊ͷ‫ ܭ‬4 ໊ͱ͢Δɽ֤ೖྗ͜ͱ΋ՄೳͰ͋Δɽ

࿩ऀʹΑΔ ATR Իૉόϥϯεจதͷ A - I ηοτͷ‫ ܭ‬450 จΛ

2 ͭ໨ͷ఺ʹؔͯ͠͸ɼຊใࠂͰ͸ɼೖྗԻ੠͔Β߹੒Ի੠༻͍ͯɼೖྗ࿩ऀґଘΞϥΠϝϯτ༻ HMM Λֶश͢Δɽ·ͨɼ΁ͱ൓өͤ͞Δ‫ܧ‬ଓ௕ͷ୯Ґͱͯ͠ɼঢ়ଶ‫ܧ‬ଓ௕ɼԻૉ‫ܧ‬ଓ௕ɼ֤ೖྗ࿩ऀͷԻ੠σʔλΛ༻͍ͯ໨ඪ࿩ऀ HMM ΛదԠ͢Δ͜Ϟʔϥ‫ܧ‬ଓ௕ͷ࢖༻Λ‫ݕ‬౼͢ΔɽԻૉ‫ܧ‬ଓ௕͓ΑͼϞʔϥ‫ܧ‬ଓͱͰɼదԠॲཧʹ‫ͮ͘ج‬ೖྗ࿩ऀґଘΞϥΠϝϯτ༻ HMM ΋௕Λ༻͍Δࡍʹ͸ɼ߹੒Ի੠ύϥϝʔλੜ੒࣌ʹ͓͍ͯɼԻૉ

ߏங͢ΔɽదԠσʔλͱͯ͠ɼ֤ೖྗ࿩ऀʹΑΔ 450 จΛ࢖༻಺͓ΑͼϞʔϥ಺ͷ HMM ঢ়ଶ‫ྻܥ‬͸ɼԻૉ / Ϟʔϥ‫ܧ‬ଓ௕͕͠ɼదԠจ਺ΛมԽͤ͞Δ͜ͱͰɼదԠॲཧʹඞཁͳσʔλྔ༩͑ΒΕ͍ͯΔ৚݅ͷԼͰͷঢ়ଶ‫ܧ‬ଓ௕Ϟσϧͷ໬౓࠷େԽ‫ج‬ʹ͍ͭͯ‫ݕ‬౼͢Δɽ֤ೖྗ࿩ऀʹରͯ͠ɼATR Իૉόϥϯεจதͷ J ηοτ 53 จΛධՁσʔλͱͯ͠࢖༻͢Δɽ

—3—

ߦ͏ɽΞϥΠϝϯτ༻ HMM ʹ͸ɼೖྗ࿩ऀ HMM Λ༻͍ɼ‫ܧ‬ଓ௕୯Ґ͸Իૉ‫ܧ‬ଓ௕ͱ͢Δɽ·ͨɼ֤ೖྗ࿩ऀʹର͢Δ༗੠ / ແ੠৘ใෆҰக཰ ʢ U/V ෆҰக཰ ʣ ʹ͍ͭͯ΋ௐࠪ͢Δɽ
F0 [ Hz ]4. 2 ‫ܧ‬ଓ௕ʹؔ͢ΔධՁ࣮‫ݧ‬
4. 2. 1 ٬‫؍‬తධՁ࣮‫݁ݧ‬Ռ
ਤ 3 ( a ) ʙ ( c ) ɼదԠ HMM ͓Αͼ໨ඪ࿩ऀ HMM ʹΑΓ‫ܧ‬ଓ௕Λܾఆͨ͠ࡍͷϝϧέϓετϥϜ࿪ΈΛࣔ͢ɽ·ͨɼࢀߟͱͯ͠ɼ໨ඪ࿩ऀ HMM ʹΑΓ‫ܧ‬ଓ௕Λܾఆͨ͠ϝϧέϓετ
ϥϜʹର͢Δ࿪Έ΋‫ͨ͠ࢉܭ‬ɽͨͩ͠ɼ֤ਤʹ͓͚ΔϦϑΝϨ
ϯεͷੜ੒࣌ʹ༻͍ͨ‫ܧ‬ଓ௕୯Ґ͸ɼͦΕͧΕɼ ( a ) ঢ়ଶ‫ܧ‬ଓ
Frame index௕ɼ ( b ) Իૉ‫ܧ‬ଓ௕ɼ ( c ) Ϟʔϥ‫ܧ‬ଓ௕୯ҐͰ͋Δɽશͯͷ‫ܧ‬

ਤ 2 ֤ F0 ‫ྻܥ‬ͷྫʢ্ɿೖྗԻ੠ͷ F0 ‫ྻܥ‬ɼதɿ໨ඪ࿩ऀ HMM ͔ଓ௕୯Ґʹ͓͍ͯɼ1 จదԠͷదԠ HMM Λ༻͍Δ͜ͱͰɼ໨

ΒೖྗԻ੠ͷ‫ܧ‬ଓ௕ʹ‫͖ͮج‬ੜ੒͞ΕΔ F0 ‫ྻܥ‬ɼԼɿԻߴม‫׵‬ඪ࿩ऀ HMM Λ༻͍ͨ৔߹ͱൺֱͯ͠ɼϝϧέϓετϥϜ࿪ΈΛࢪͨ͠ೖྗ࿩ऀͷ F0 ‫ྻܥ‬ʢ఺ઢʣͱɼ͞Βʹ༗੠ / ແ੠৘ใ͕େ͖͘‫ݮ‬গ͢Δɽ·ͨɼదԠจ਺Λ૿Ճͤ͞Δ͜ͱͰɼ؇΍ͷิਖ਼Λࢪͨ͠ F0 ‫ྻܥ‬ʢ࣮ઢʣʣ
Fig. 2 Example of F0 contours (top: F0 contour of input speech,͔Ͱ͸͋Δ͕ɼ͞ΒʹϝϧέϓετϥϜ࿪Έ͕‫ݮ‬গ͢Δ܏޲͕

middle: F0 contour generated from target speaker’s HMM,‫ݟ‬ΒΕΔɽ͜ͷ͜ͱ͔ΒɼΞϥΠϝϯτ༻ HMM ͷߏஙʹ͓͍bottom: F0 contour of input speech after range transfor-ͯɼϞσϧదԠ͸༗ޮͰ͋Γɼ1 จͱ͍͏‫ݶ‬ΒΕͨదԠσʔλmation (dotted line) and that after unvoiced/voiced com-ྔʹ͓͍ͯ΋ɼͦͷޮՌ͕ಘΒΕΔ͜ͱ͕෼͔Δɽ

pensation ( solid line ) ).

Ի੠σʔλͷαϯϓϦϯάप೾਺͸ 16 kHz ͱ͢ΔɽεϖΫτϧύϥϝʔλͱͯ͠ɼSTRAIGHT ෼ੳ [20] ͰಘΒΕΔεϖ
ΫτϧแབྷΛϞσϧԽͨ͠ 0 ͔࣍Β 24 ࣍ͷϝϧέϓετϥϜ܎਺Λ࢖༻͢ΔɽԻ‫ݯ‬ύϥϝʔλͱͯ͠ɼର਺ F0 ͓Αͼ 5 प೾਺ଳҬʹ͓͚Δฏ‫ۉ‬ඇप‫ظ‬੒෼ [21] Λ࢖༻͢ΔɽϑϨʔϜγϑτ͸ 5 ms ͱ͢ΔɽHMM ͸ 5 ঢ়ଶ left-to-right ‫͠ͱܕ‬ɼ௨ৗͷֶशॲཧ [5] ʹ‫͖ͮج‬ɼίϯςΩετґଘԻૉ HMM Λֶश͢Δɽ‫ܧ‬ଓ௕੍‫ޚ‬ͷධՁʹ͓͍ͯ͸ɼೖྗԻ੠ͷ‫ܧ‬ଓ௕Λ൓өͤ͞
Δ୯Ґͱͯ͠ɼঢ়ଶ‫ܧ‬ଓ௕ʢ State ʣɼԻૉ‫ܧ‬ଓ௕ʢ Phone ʣɼϞʔϥ‫ܧ‬ଓ௕ ʢ Mora ʣ Λ࢖༻͢ΔɽΞϥΠϝϯτ༻ HMM ͱͯ͠ɼೖྗ࿩ऀ HMM ʢ 450 จʹֶͯश ʣ ɼదԠ HMM ʢ 1 จ
͔Β 450 จ·ͰదԠจ਺ΛมԽʣɼ͓Αͼ໨ඪ࿩ऀ HMM ʢ దԠલͷϞσϧʹରԠ ʣ Λ༻͍Δɽ٬‫؍‬ධՁͱͯ͠ɼೖྗ࿩
ऀ HMM Ͱ‫ܧ‬ଓ௕Λܾఆͨ͠ࡍͷ߹੒Ի੠ͷϝϧέϓετϥϜ
ΛϦϑΝϨϯεͱͯ͠ɼଞͷΞϥΠϝϯτ༻ HMM Ͱ‫ܧ‬ଓ௕Λܾఆͨ͠ࡍͷ߹੒Ի੠ʹର͢ΔϝϧέϓετϥϜ࿪ΈΛଌఆ͢ΔɽΞϥΠϝϯτ୯ҐຖʹϝϧέϓετϥϜ࿪ΈΛଌఆ͢Δ͜
ͱͰɼ֤‫ܧ‬ଓ௕୯Ґؒͷྨࣅੑ΍దԠจʹԠͨ͡దԠॲཧͷޮՌʹ͍ͭͯௐࠪ͢Δɽ·ͨɼओ‫؍‬ධՁͱͯ͠ɼ3 छྨͷ‫ܧ‬ଓ௕୯Ґʹରͯ͠ɼ໨ඪ࿩ऀ HMM ʢ Target ʣ ɼ1 จదԠʹΑΔద
Ԡ HMM ʢ 1 utterance ʣ ɼ56 จదԠʹΑΔదԠ HMM ʢ 56

utterances ʣ ɼೖྗ࿩ऀ HMM ʢ Reference ʣ ΛΞϥΠϝϯτ
༻ HMM ͱͯ͠࢖༻ͨ͠ࡍͷ߹੒Ի੠‫ ܭ‬12 छྨ ʢ ‫ܧ‬ଓ௕୯Ґ
͕ 3 छྨͱΞϥΠϝϯτ༻ HMM ͕ 4 छྨͷ૊Έ߹Θͤ ʣ Λൺֱ͢Δɽ߹੒Ի੠ͷࣗવੑʹؔ͢Δ MOS ධՁ࣮‫ͱݧ‬ɼೖྗԻ੠ΛϦϑΝϨϯεͱͨ͠ࡍͷӆ཯ͷ໚฿ੑʹؔ͢Δ DMOS ධՁΛߦ͏ɽ

F0 ‫ޚ੍ྻܥ‬ͷධՁʹ͓͍ͯ͸ɼ༗੠ / ແ੠৘ใิਖ਼ॲཧͷޮՌΛௐࠪ͢Δɽิਖ਼͋Γʢ w/ mod ʣͱิਖ਼ͳ͠ʢ w/o mod ʣͷ 2 ख๏ʹରͯ͠ɼ߹੒Ի੠ͷࣗવੑʹؔ͢Δରൺֱ࣮‫ݧ‬Λ࣍ʹɼ‫ܧ‬ଓ௕୯Ґʹண໨͢Δͱɼਤ 3 ( a ) ʙ ( c ) , ͔ΒɼϦϑΝϨϯεੜ੒࣌ʹ༻͍ͨ‫ܧ‬ଓ௕୯Ґͱಉ͡୯ҐΛ༻͍ͨࡍʹɼ࠷΋ϝϧέϓετϥϜ࿪Έ͕খ͘͞ͳΔ܏޲͕‫ݟ‬ΒΕΔɽ·ͨɼԻૉ‫ܧ‬ଓ௕୯Ґʹؔͯ͠͸ɼϦϑΝϨϯεੜ੒࣌ʹঢ়ଶ‫ܧ‬ଓ௕ ( ਤ 3 ( a ) ) ·ͨ͸Ϟʔϥ‫ܧ‬ଓ௕ ( ਤ 3 ( c ) ) Λ༻͍ͨࡍʹ͓͍ͯ΋ɼൺֱతϝϧέϓετϥϜ࿪Έ͕খ͍͜͞ͱ͕෼͔ΔɽಛʹɼϦϑΝϨϯεੜ੒࣌ʹঢ়ଶ‫ܧ‬ଓ௕Λ༻͍ͨࡍ ( ਤ 3 ( a ) ) ʹ͓͍ͯ͸ɼϦϑΝϨϯεͱಉ͡ঢ়ଶ‫ܧ‬ଓ௕Λ༻͍ͨ৔߹ͱൺֱ
ͯ͠΋ɼಉఔ౓ʹখ͞ͳϝϧέϓετϥϜ࿪Έ͕ಘΒΕΔ͜ͱ
͕෼͔Δɽ

4. 2. 2 ओ‫؍‬తධՁ࣮‫݁ݧ‬Ռࣗવੑʹؔ͢Δ MOS ධՁ݁ՌΛਤ 4 ʹɼӆ཯ͷ໛฿ੑʹ
ؔ͢Δ DMOS ධՁΛਤ 5 ʹࣔ͢ɽਤ 4 ͱਤ 5 ͔Βɼࣗવੑͱ໛฿ੑͷ྆໘ʹ͓͍ͯɼదԠ HMM ( “ 1 utterance ”ͱ “ 56utterance ” ) ͷείΞ͸ɼ໨ඪ࿩ऀ HMM ( “ target ” ) ͷείΞΛ্ճΔ͜ͱ͕෼͔Δɽ·ͨɼϞʔϥ‫ܧ‬ଓ௕୯ҐΛ࢖༻ͨ͠৔߹ͷ໛฿ੑͷείΞΛআ͍ͯɼ56 จʹΑΔదԠ HMM ( “ 56utterance ” ) ͸ɼೖྗ࿩ऀ HMM ( “Reference” ) ͱಉ౳ͷࣗવੑ͓Αͼ໛฿ੑ͕ಘΒΕΔ͜ͱ͕෼͔ΔɽҎ্ͷ݁Ռ͔ΒɼదԠॲཧʹΑΔΞϥΠϝϯτ༻ HMM ͷߏங͸ɼࣗવੑͱ໛฿ੑΛվળ͢Δ্ͰޮՌతͳख๏Ͱ͋Δͱ͍͑ΔɽϞʔϥ‫ܧ‬ଓ௕୯ҐΛ࢖༻ͨ͠ࡍʹ͸ɼଞͷ‫ܧ‬ଓ௕୯ҐΛ࢖༻ͨ͠৔߹ͱൺֱͯ͠ɼ໛฿ੑ͕ྼԽ͢Δ܏޲͕‫ݟ‬ΒΕΔɽϞʔϥ‫ܧ‬ଓ௕୯ҐΛར༻ͨ͠ࡍʹ͸ɼଞͷ‫ܧ‬ଓ௕୯ҐΛ࢖༻ͨ͠৔߹ͱൺֱͯ͠ɼ߹੒࣌ʹ͓͚Δ HMM ঢ়ଶ‫ྻܥ‬͸໨ඪ࿩ऀ HMM ͷঢ়ଶ‫ܧ‬ଓ௕ϞσϧͷӨ‫ڹ‬ΛΑΓ‫͘ڧ‬ड͚Δɽͦͷ݁Ռɼ໛฿ੑͷྼԽ͕ੜ͡Δͱߟ͑ΒΕΔɽ
ࣗવੑͱ໛฿ੑʹؔͯ͠ɼ࠷΋ߴ͍είΞ͕ಘΒΕ͍ͯΔԻ
ૉ୯ҐΞϥΠϝϯτΛ༻͍ͨ “ Reference ” ͷείΞͱɼଞख๏
ͷείΞͱͷؒʹͯɼt ‫ݕ‬ఆΛߦͬͨ݁ՌΛද 1 ʢ ࣗવੑʹؔ
͢Δ MOS ʹର͢Δ݁Ռ ʣ ͱද 2 ʢ ໛฿ੑʹؔ͢Δ DMOS ʹର͢Δ݁Ռ ʣ ʹࣔ͢ɽ1 จΛ༻͍ͨదԠ HMM ʹண໨͢Δͱɼ

—4—

95% conﬁdence interval

State alignment
Phone alignmentMora alignment

450

Target

The number of adaptation utterance

1 utterance 56 utterance Referenceਤ 4 ࣗવੑʹؔ͢Δ MOS ධՁ݁Ռ(a) ঢ়ଶ‫ܧ‬ଓ௕

Fig. 4 Mean opinion score on speech quality.
State alignment
Phone alignmentMora alignment

95% conﬁdence interval

450

Target

The number of adaptation utterance

1 utterance 56 utterance Referenceਤ 5 ӆ཯ͷ໛฿ੑͷ DMOS ධՁ݁Ռ(b) Իૉ‫ܧ‬ଓ௕

Fig. 5 Degradation mean opinion score on prosodical mimic ability.

State alignment
Phone alignmentMora alignmentද1ࣗવੑʹؔ͢Δ MOS είΞʹ͓͚ΔɼԻૉΞϥΠϝϯτ

Table 1Λ༻͍ͨ “ Reference ” ʹର͢Δ֤ख๏ͷ p ஋P-values compared to “Reference” with the phoneme-level alignment in term of the MOS scores for speech
quality.
State

Phone

Mora

Target8.77e−10

6.89e−6

6.96e−7

1 utterance

7.58e−40.02590.128(c) Ϟʔϥ‫ܧ‬ଓ௕

56 utterance

0.389

0.490

0.102

ਤ 3 ֤‫ܧ‬ଓ௕୯Ґʹ͓͚ΔϝϧέϓετϥϜ࿪Έ ( ࣮ઢ ( ࠇ৭ ) ɼ࣮

Reference

0.187

\

0.283

450
The number of adaptation utteranceઢ ( փ৭ ) ɼഁઢ͸ͦΕͧΕɼ໨ඪ࿩ऀ HMM Λ༻͍ͯঢ়ଶɼ

ද2໛฿ੑʹؔ͢Δ DMOS είΞʹ͓͚ΔɼԻૉΞϥΠϝϯτ

ԻૉɼϞʔϥͷ‫ܧ‬ଓ௕୯ҐΛܾఆͨ͠৔߹ͷ࿪ΈΛද͢ɽ )Fig. 3 Mel-cepstral distortion in each alignment unit ( Black line,Λ༻͍ͨ “Reference”ʹର͢Δ֤ख๏ͷ p ஋
Table 2 P-values compared to “Reference” with the phoneme-

Gray line, and dotted line indicate the distortion with thelevel alignment in term of the DMOS scores for mimic

state-level, phoneme-level, and mora-level alignments de-ability.

termined using the target speaker’s HMM. ) .

State

Phone

Mora

Target3.07e−94.03e−10

1.05e−14

1 utterance

3.91e−4

0.04252.73e−5ҙਫ४ 1 % ʹͯ༗ҙ͕ࠩೝΊΒΕΔɽ·ͨɼ໛฿ੑʹؔͯ͠͸ɼ56 utterance

0.680

0.365

6.73e−5ঢ়ଶ୯Ґ͓ΑͼϞʔϥ୯ҐΛ༻͍ͨࡍͷείΞͱͷؒʹɼ༗ҙ

Reference

0.675

\

0.0376ࣗવੑʹؔͯ͠͸ɼঢ়ଶ୯ҐΛ༻͍ͨࡍͷείΞͱͷؒʹɼ༗

ਫ४ 1 % ʹͯ༗ҙ͕ࠩೝΊΒΕΔɽԻૉ୯ҐΛ༻͍ͨࡍʹ͸ɼࣗવੑ͓Αͼ໛฿ੑͷ྆໘ʹ͓͍ͯ༗ҙ͕ࠩೝΊΒΕͳ͍ɽ͜ஶʹେ͖͍ɽ௨ৗɼ༗੠۠ؒʹ͓͚Δύϫʔ͸ɼແ੠۠ؒͷύͷ͜ͱ͔ΒɼԻૉ୯Ґͷ࢖༻͕࠷΋ޮՌతͰ͋Δͱ൑அ͢Δɽ

ϫʔͱൺֱͯ͠େ͖͍܏޲ʹ͋ΔɽͦͷͨΊɼ༗੠۠ؒʹ͓͚

4. 3 ༗੠/ແ੠ิਖ਼ॲཧͷධՁ݁ՌΔεϖΫτϧͱແ੠Ի‫ݯ‬Λ༻͍ͯԻ੠Λ߹੒ͨ͠৔߹ɼࣗવੑ

ਤ 6 ʹ U/V ෆҰக཰Λɼਤ 7 ʹࣗવੑʹؔ͢ΔϓϦϑΝϨϯ

͕େ͖͘ྼԽ͢Δɽ͜ͷ͜ͱ͔ΒɼSpeaker 2 ͷΑ͏ʹɼ໨ඪεείΞΛࣔ͢ɽਤ 6 ΑΓɼશͯͷೖྗ࿩ऀʹ͓͍ͯɼU/V ෆ࿩ऀ HMM ʹΑΔ༗੠͕۠ؒೖྗԻ੠ͷແ੠۠ؒʹଟׂ͘Γ౰Ұக͕ੜ͍ͯ͡Δ͜ͱ͕෼͔Δɽ·ͨɼਤ 7 ΑΓɼ4 ໊த 3 ໊

ͨΔࡍʹ͸ɼU/V ิਖ਼ॲཧʹΑΔࣗવੑͷվળޮՌ্͕ঢ͢Δ

ͷೖྗ࿩ऀʹ͓͍ͯɼU/V ิਖ਼ॲཧʹΑΓࣗવੑ͕վળ͞Εͱߟ͑ΒΕΔɽͨ͜ͱ͕෼͔Δɽͳ͓ɼSpeaker 2 Ͱ͸ɼଞͷ࿩ऀͱൺֱ͠ɼ

U/V ෆҰக཰͸தఔ౓Ͱ͋Δ΋ͷͷɼࣗવੑͷվળޮՌ͸‫ݦ‬

—5—

20
V to U
U to VU/V error rate [%]

15

10

5

0

Speaker 1

Speaker 2

Speaker 3

Speaker 4ਤ 6 ༗੠/ແ੠ෆҰக཰

Fig. 6 U/V Error rate.
95% confidence interval

w/o mod w/ mod w/o mod w/ mod w/o mod w/ mod w/o mod w/ mod

Speaker 3
Speaker 4
Speaker 1
Speaker 2
ਤ 7 ࣗવੑʹؔ͢Δओ‫؍‬ධՁ݁Ռ

Fig. 7 Preference score for speech quality.

5. · ͱ ΊຊใࠂͰ͸ɼ໨ඪͱ͢Δಛఆ࿩ऀͷԻ੠߹੒ॲཧʹ͓͍ͯɼॴ๬ͷӆ཯Λ࣋ͭ߹੒Ի੠Λ࣮‫͢ݱ‬Δ࿮૊ΈΛ໨ࢦ͠ɼԻ੠ೖ
ྗʹΑΔӆ཯੍‫ػޚ‬ೳΛ༗͢Δ HMM Ի੠߹੒๏ΛఏҊͨ͠ɽఏҊ๏ʹ͓͍ͯɼೖྗԻ੠ͱ߹੒Ի੠ؒʹ͓͚Δӆ཯ಛ௃ͷෆҰக͕࠷ऴతͳ߹੒Ի੠ʹ༩͑ΔӨ‫ڹ‬Λௐࠪ͢ΔͨΊʹɼ‫ܧ‬ଓ௕੍‫͍༻ʹ࣌ޚ‬Δ‫ܧ‬ଓ௕୯ҐͱΞϥΠϝϯτ༻ HMM ͷߏங๏
ʹؔ͢Δ‫ݕ‬౼ͱɼF0 ‫͚͓ʹޚ੍ྻܥ‬Δ༗੠ / ແ੠৘ใͷิਖ਼ॲཧʹؔ͢Δ‫ݕ‬౼Λߦͬͨɽ࣮‫݁ݧ‬Ռ͔Βɼ1 ) Իૉ୯ҐʹΑΔ‫ܧ‬ଓ௕੍‫͕ޚ‬༗ޮͰ͋Δ͜ͱɼ2 ) ϞσϧదԠॲཧʹΑΔΞϥΠϝ
ϯτ༻ HMM ͷߏங͸༗ޮͰ͋ΓɼదԠจ਺͕ 1 จͷ৔߹ʹ͓͍ͯ΋ɼࣗવੑ͓Αͼ໛฿ੑͷ྆໘ʹ͓͍ͯେ͖ͳվળޮՌ͕
ಘΒΕΔ͜ͱɼ3 ) ༗੠ / ແ੠৘ใͷิਖ਼ॲཧʹΑΓࣗવੑ͕վ
ળ͞ΕΔ͜ͱΛࣔͨ͠ɽࠓ‫ޙ‬͸ɼԻ੠ೖྗʹΑΔύϫʔ‫੍ྻܥ‬‫ޚ‬΍੠੍࣭‫ޚ‬ͷಋೖʹऔΓ૊Ήɽँࣙຊ ‫ ڀ ݚ‬ͷ Ұ ෦ ͸ ɼJSPS Պ ‫ ݚ‬අ 26280060 ͓ Α ͼ

24300073 ͷॿ੒Λड͚࣮ࢪͨ͠΋ͷͰ͋Δɽจ‫ݙ‬[1] ಙా ‫ܙ‬Ұ, ϒϥοΫ Ξϥϯ, “Ի੠߹੒‫ڀݚ‬΋‫ڠ‬ௐͱ‫ڝ‬૪ͷ࣌୅ʹ : The Blizzard Challenge,” ೔ຊԻ‫ֶڹ‬ձࢽ, Vol. 62, No.
6, pp. 466–472, Jun., 2006.
[2] CeVIO Creative Studio http://cevio.jp[3] H. Kenmochi, and H. Ohshita, “VOCALOID - Commercial
singing synthesizer based on sample concatenation,” Proc.
INTERSPEECH, pp. 4011–4012, Antwerp, Belgium, Aug.,2007.

[4] H. Zen, K. Tokuda, and A. W. Black, “Statistical parametric speech synthesis,” Speech Commun., Vol. 51, No. 11, pp.
1039–1064, Apr., 2009.
[5] ٢ଜ ‫ࠀو‬, ಙా ‫ܙ‬Ұ, ӹࢠ ‫࢙و‬, খྛ ོ෉, ๺ଜ ਖ਼, “HMM ʹ‫ͮ͘ج‬Ի੠߹੒ʹ͓͚ΔεϖΫτϧɾϐονɾ‫ܧ‬ଓ௕ͷಉ࣌ϞσϧԽ,” ిࢠ৘ใ௨৴ֶձ࿦จࢽ, Vol. J83-D-II, No. 11, pp.
2099–2107, Nov., 2000.
[6] Ѩ෦ ‫ڡ‬৳, ਫ໺ ल೭, ਫ໺ ཧ, ໺ా ‫ت‬ত, ߴ‫ ڮ‬හ, தቇ ৴໻,“Ի੠σβΠϯπʔϧ Sesign,” ిࢠ৘ใ௨৴ֶձ࿦จࢽ, Vol.
J84-D-II, No. 6, pp. 927–935, Jun., 2000.
[7] T. Nose, J. Yamagishi, T. Masuko, and T. Kobayashi, “A
style control technique for HMM-based expressive speechsynthesis,” IEICE Trans., Inf. and Syst., Vol. E90-D, No.
9, pp. 1406–1413, Sept., 2007.
[8] த໺ ྙ༃, ‫ޙ‬౻ ਅ޹, “VocaListener : ϢʔβՎএͷԻߴ͓ΑͼԻྔΛਅࣅΔՎ੠߹੒γεςϜ,” ৘ใॲཧֶձ࿦จࢽ, Vol. 52,
No. 12, pp. 3853–3867, Dec., 2011.
[9] த໺ ྙ༃, ‫ޙ‬౻ਅ޹, “VocaListener2 : ϢʔβՎএͷԻߴɾԻྔʹՃ͑ͯ੠৭มԽ΋ਅࣅΔՎ੠߹੒γεςϜ,” ৘ใॲཧֶձ
࿦จࢽ, Vol. 54, No. 6, pp. 1771–1783, Jun., 2013.
[10] Y. Stylianou, O. Cappe, and E. Moulines, “Continuous
probabilistic transform for voice conversion,” IEEE Trans.
Speech and Audio Processing, Vol. 6, No. 2, pp. 131–142,
Mar., 1998.
[11] T. Toda, A. W. Black, and K. Tokuda, “Voice conversion
based on maximum likelihood estimation of spectral parameter trajectory,” IEEE Trans. Audio, Speech, and Language, Vol. 15, No. 8, pp. 2222–2235, Nov., 2007.
[12] T. Nose, Y. Ota, and T. Kobayashi, “HMM-based voice
conversion using quantized F0 context,” IEICE Trans. Inf.
and Syst., Vol. E93-D, No. 9, pp. 2483–2490, Sept., 2010.
[13] K. Tokuda, T. Yoshimura, T. Masuko, T. Kobayashi, andT. Kitamura, “Speech parameter generation algorithms for
HMM-based speech synthesis,” Proc. ICASSP., pp. 1315–
1318, Istanbul, Turkey, Jun., 2000.
[14] T. Toda, and K. Tokuda, “A speech parameter generation algorithm considering global variance for HMM-based
speech synthesis,” IEICE Trans., Vol. E90-D, No. 5, pp.
816–824, May, 2007.
[15] J. Yamagishi and T. Kobayashi, “Average-voice-based
speech synthesis using HSMM-based speaker adaptation
and adaptive training,” IEICE Trans. Inf. and Syst., Vol.
E90-D, No. 2, pp. 533–543, Feb., 2007.
[16] K. Yu and and S. Young, “Continuous F0 modeling for
HMM based statistical parametric speech synthesis,” IEEE
Trans. Audio, Speech and Language., Vol. 19, No. 5, pp.
1071–1079, Jul., 2011.
[17] K. Tanaka, T. Toda, G. Neubig, S. Sakti, and S. Nakamura,“A hybrid approach to electrolaryngeal speech enhancement
based on spectral subtraction and statistical voice conversion,” Proc. INTERSPEECH, pp. 3067–3071, Lyon, France,
Sept., 2013.
[18] P. Taylor, Text-To-Speech synthesis., Cambridge Univ.
Press, 2009.
[19] Ѩ෦ ‫ڡ‬৳, ೏ࡔ ๕య, കా ఩෉, ‫ ݪ܂‬ঘ෉, “‫༻ڀݚ‬೔ຊ‫ޠ‬Ի੠ɹσʔλϕʔεར༻ղઆॻʢ࿈ଓԻ੠σʔλฤʣ,” ATR ςΫχΧϧϨϙʔτ, TR=1-0166, Sept., 1990.
[20] H. Kawahara, I. Matsuda-Katsuse, and A. D. Cheveigne,“Restructuring speech representations using a pitchadaptive time-frequency smoothing and an instantaneousfrequency-based F0 extraction: Possible role of a repetitive
structure in sounds,” Speech Commun., Vol. 27, No. 3–4,
pp. 187–207, Apr., 1999.
[21] H. Kawahara, Jo Estill, and O. Fujimura, “Aperiodicity extraction and control using mixed mode excitation and groupdelay manipulation for a high quality speech analysisɼ modiﬁcation and synthesis system STRAIGHT,” In MAVEBA
2001, pp. 1–6, Firenze, Italy, Sept., 2001.

—6—