第22回 ロボット聴覚特集 - 奥乃研究室 - 京都大学

社 団 法 人 人 工 知 能 学 会Japanese Society forArtificial Intelligence人 工 知 能 学 会JSAI Technical ReportSIG-CHallege-0522-17 (10/15)Evaluation of Missing Feature Theory Based Automatic Speech Recognitionfor Simultaneous Speech Sentences 1 Jean-Marc Valin 2 3 3 3 1 1 1Shunichi YAMAMOTO 1 ,Jean-MarcVALIN 2 , Kazuhiro NAKADAI 3 ,HiroshiTSUJINO 3 ,Kazunori KOMATANI 1 ,TetsuyaOGATA 1 ,andHiroshiG.OKUNO 11 , Graduate School of Informatics, Kyoto University2 Dept. of Electrical Engineering and Computer Engineering, Université de Sherbrooke3 () , Honda Research Institute Japan, Co., Ltd.{shunichi,komatani,ogata,okuno}@kuis.kyoto-u.ac.jp, jean-marc.valin@usherbrooke.ca,{nakadai,nakano,tsujino}@jp.honda-ri.comAbstractA robot in the real world usually hears mixturesof sounds. To achieve such a robot audition system,the integrtion of sound source separation(SSS) and automatic speech recognition (ASR)is necessary. We propose to use the missingfeature theory (MFT) as an interface with highinteroperability for the integration. The mainadvantage of this approach resides in the factthat the ASR with a clean acoustic model canadapt the distortion of separated speech by consultinga missing feature mask (MFM). In ourMFT-based robot audition system, we developeda microphone array SSS system to outputseparated speech with a MFM generated withoutany prior knowledge, and we used MultibandJulius, which supported stochastic languagemodels and recognized speech fast, forthe MFT-based ASR to recognize the separatedspeechbyusingtheMFM.Weevaluatethe robot audition system working with the humanoidSIG2. As a result, we showed the improvementin word correct rates and processingspeed through speech recognition of the mixturesof three sentences as well as those of threeisolated words.1 AURORA [1, 2] HMM [3, 4] 8ch [5] [6] 2 101