第22回ロボット聴覚特集 - 奥乃研究室 - 京都大学

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ロボットと一人の話者との対話を前提としたものであり, 対話制御の対象としたものは,ロボット本体の簡単な動作と情報家電の制御であった.しかしながら, 我々の日常生活環境では, 複数のユーザとロボットとの対話のような場面が用意に考えられ,そのような場面では,ユーザ同士の会話にロボットが反応し, 誤動作を起こす可能性がある. 今後は, 現在使用している画像処理部分に,ユーザの視線や顔の方向検出を導入し,より詳細な対話制御を行うように,ロボット内の計算資源の拡張を含めたシステムの拡張をおこなう.また, 音声認識の失敗による誤動作を修正するために, 緊急時対応の音声コマンドの導入を進めていく予定である.参考文献1) http://www.nedo.go.jp/roadmap/index.html2) K.Nakadai,K.Hidai,H.Mizoguchi, H.G.Okuno andH.Kitano, “Real-Time Auditory and Visual Mutiple-ObjectTracking for Humanoid”, Proc of IJCAI2001, pp1424-1432,2001.3) http://www.incx.nec.co.jp/robot/4) 松日楽信人 , 小川英樹 , 吉見卓 ,” 人と共存する生活ロボット”, 東芝レビュー, Vol.60,No.7, pp112-115,20055) http://www.hqrt.hitachi.co.jp/merl/robot/6) F. Asano, K. Yamamoto, I. Hara, J. Ogata, T. Yoshimura,Y. Motomura, N. Ichimura and H. Asoh,: “Detection andSeparation of Speech EventUsing Audio and Video InformationFusion and Its Application to Robust Speech Interface",Eurasip Journal on Applied Signal Processing, 2004,11, pp.1727-1738 ,2004.7) I. Hara, F. Asano, H. Asoh, J. Ogata, N. Ichimura, Y.Kawai, F. Kanehiro, H. Hirukawa and K. Yamamoto:“Robust Speech Interface Based on Audioand Video InformationFusion for Humanoid HRP-2",Proc. of IROS2004, pp. 2404-2410. 2004.8) K. Yamamoto, F. Asano, I. Hara, J. Ogata, M. Goto, H.Furukawa, T. Kamashima and N. Kitawaki,: “Real-timeImplementation and Evaluation of Speech Event Detectionand SeparationBased on the Fusion of Audio and VideoInformation", Proc.s of GSPx 2004 , 2004.9) M. Goto, R. Neyama and Y. Muraoka,: “RMCP:RemoteMusic Control Protocol | Design and Applications |", Proc.of the 1997 Int. Computer Music Conference, pp.446-449 ,1997.10) F. Asano, Y. Motomura, H. Asoh, T. Yoshimura,N. Ichimuraand S. Nakamura: “Fusion of Audio and Video Informationfor Detecting Speech Event", Proce. of Fusion2003, pp. 386-393, 2003.11) D. Comaniciu, V. Ramesh and P. Meer: “Kernel-based objecttracking", IEEE Trans. on Pattern Analysis MachineIntelligence, 25, 5, pp. 564-575,2003.12) D. H. Johnson and D. E. Dudgeon,: Array Signal Processing,Prentice Hall, 1993.13) 河原達也 , 李晃伸 :” 連続音声認識ソフトウエア Julius”,人工知能学会誌 , Vol.20, No.1, pp.41-49, 2005.14) C.J. Leggetter and P. C. Woodland, “Maximum likelihoodlinear regression for speaker adaptation of continuous densityhidden Markov models", Computer Speech and Language,9, 2, pp. 171-185 ,1995.15) J. L. Gauvain and C. H. Lee, “Maximum a posteriori estimationfor multivariate Gaussian mixture observations ofMarkov chains", IEEE Transactions on Speech and AudioProcessing, 2, 2, pp. 291-298.1994.16) E. Thelen, X. Aubert and P. Beyerlein: “SpeakerAdaptationin the Philips System for Large Vocabulary ContinuousSpeech Recognition", Proc. of ICASSP '97, pp.1035-1038 ,1997.17) 河原達也 , 武田一哉 , 伊藤克亘 , 李晃伸 , 鹿野清宏 , 山田篤 :” 連続音声認識コンソーシアムの活動報告及び最終版ソフトウェアの概要 ", SP2003-169, NLC2003-106(SLP-49-57), 電子情報通信学会技術研究報告 , 2003.88
社団法人人工知能学会Japanese Society forArtificial Intelligence人工知能学会JSAI Technical ReportSIG-CHallege-0522-15 (10/15)Sound Source Localization robust to variations of environmentsusing microphones mounted to head of robot , , , Toshiaki Kubo Naoya Mochiki Tetsuji Ogawa Tetsunori Kobayashi Department of Computer Science, Waseda UniversityAbstractA sound source localization method using statisticalpattern recognition is extended so thatit works robustly in various environmentsIn our previous work, we proposed new types ofsound source localization methods using robotmounting microphones, which are free fromHRTF (Head Related Transfer Function) estimation.This method is performed with statisticalpattern recognition which employs theratio of spectra amplitude obtained for pairs ofmicrophones as feature parameters. It workswell whatever the sound source is, because thefeature is completely sound-source-invariant.However, it is slightly sensitive to the variationsof environmentsIn order to solove this problem, HLDA (HeteroscedasticLinear Discriminant Analysis) isapplied to extract environment-invariant featuresExperimentalresults show perfect performanceof the proposed method with HLDAfeature extraction.1 4 [1][2][3] [3] MLLR HLDA (Heteroscedastic Linear DiscriminantAnalysis) [5] 23 HLDA 452 2.1 2 4 Figure 1 RF-Mic(Right-Front-Microphone)LF-Mic(Left-Front-Microphone) RR-Mic(Right-Right-Microphone)LL-Mic(Left-Left-Microphone) Audiotechnica ATM15a 89
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第22回 ロボット聴覚特集 - 奥乃研究室 - 京都大学

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第22回ロボット聴覚特集 - 奥乃研究室 - 京都大学