IPSJ SIG Technical Report Vol.2013-SLP-98 No /10/25 1,a) 1 ( Q&A ) ( ) YJVOICE Development of speech recognition and natural language processing
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1 1,a) 1 ( Q&A ) ( ) YJVOICE Development of speech recognition and natural language processing for ONSEI Assist service Abstract: ONSEI Assist is a voice dialog application for mobile devices that enables various kinds of information retrieval including train route, weather, news, web, map, shops and Q&A. It also supports mobile device operations such as alarm setting, application launch, dialing, calendar and address book search. The user input utterance is recognized by a very large vocabulary speech recognition system and sent to a server for semantic analysis which extracts the user s intention including named entities and prepares a response sentence. This article describes the speech recognition platform YJVOICE and the natural language processing technologies for ONSEI Assist. 1. Google ( ,[1]) Apple Siri(2012 3,[2]) NTT (2012 3,[3]) ios [4] 1 Yahoo! JAPAN Yahoo! JAPAN Research, Yahoo Japan Corporation, Akasaka, Minato-ku, Tokyo , Japan a) kiso@yahoo-corp.jp [5] YJVOICE 2. YJVOICE 2.1 YJVOICE (DSR, Distributed Speech Recognition) 1
2 (speex, flac ) apache HTTP https apache ( ) WFST 2.2 ( SNR ) HMM ML MPE Hadoop[6] ( ) Hadoop (HDFS, Hadoop Distributed File System) HDFS replication HMM 3 32 HMM (MFCC 12 1 MFCC 1 2 ) 2.3 ( ) ( ) SNS N ( Hadoop ) 100 pruning 30M 25M 2.4 WFST WFST [7] WFST WFST WFST [8] π(eps(min(det(c det(l min(det(g T ))))))) (1) C ( ) L G T WFST π eps ϵ WFST ( ) 4 ( 4 16 ) ( 4 8 ) 100 WFST 10 8 ( 10 5 ) ( 10 6 ) [9] 8 3 ( ϵ ) ( ) 1 8 (64bit CPU ) ( 1) 50% [10] ( 35%) 1.(/0-&1 2(/0-&1 3(/0-& '&1$#"$4#(1-"-& *#5!-64!-5!-( 107/48(5"*9!"#$%&'( )&*+,- Compressed WFST arc representation. WFST WFST (Juicer ver1.0.0, decoderlite, [11]) WFST (Julius ver.4.2.1, [12]) Julius 2
3 80 Real Time Factor and Memory Usage vs Sentence accuracy GB 2.0GB 2.3GB 2.6GB 2.9GB 3.8GB 9.9GB 10.0GB GB 9.8GB 1.7GB S.Acc (%) GB 8.9GB (memory usage) 1.5GB 1.5GB 1.7GB 1.7GB 1.8GB 55 proposed juicer ver julius ver (larger 2grams) julius ver RTF(Real Time Factor) vs RTF 1.5GB ( dev, ) ( eval, ) (SER) ( ) CPU Intel R Xeon R X5675(3.07GHz, 6, 12MB ) 128GB CentOS bit GCC ver eval (SER) (RTF, Real Time Factor ) ( GB) ( ) dev Julius 1 ( ) WFST ( ) WFST RT F = GB Juicer GB WFST ( )Julius WFST ( 0.7 ) (1) WFST (det) (min) ( ) (1 ) WFST L ( ID ) (1) [13] ID 1 ( ID) WFST WFST( WFST) WFST splice [14] (1) WFST On-the-Fly [15] G C L 3
4 WFST (1) WFST X( x) (1) x WFST [14] WFST ( ) ( ) G :{ / / } L ( ) (1) WFST pruning N (WCN, Word Confusion Networks) WCN [16], [17] pivot (1 pivot ) pivot WCN 1 1 3, [4]) ( Twitter ) 3 (ios ) SDK ios Android YJVOICE (Android 4 [5]) Android Google Play ( 50 ) ios ( 4 (Android ) 4
5 ( 5) () ( ) ( ) ( ) ( ) ( ) ( 7 15 ) ( ) ( ) ( ) ( ) ( ) Q&A( ) ( ) () 3.2 YJVOICE ( ) API ( ) ( ) 3.5 (...) ( ) ( ) ( ) 4. YJVOICE [1] M.Schuster, K.Nakajima, Japanese and Korean Voice Search, in ICASSP [2] J.R.Bellegarda, Large-Scale Personal Assistant Technology Deployment: The Siri Experience, in Inter- Speech [3],,, vol.2012-slp-93, No.4. [4] (ios ), [5] for Android, 5
6 情報処理学会研究報告 図 5 [6] [7] [8] [9] [10] [11] [12] [13] 音声アシストの利用例 Hadoop, 入手先 K.Iso, E.Whittaker, T.Emori, J.Miyake, Improvements in Japanese Voice Search, in InterSpeech C.Allauzen et al., A generalized construction of integrated speech recognition transducers, in ICASSP, P.A.Chou et al., Entropy-Constrained Vector Quantization, IEEE Trans. ASSP, vol.37, pp , D.Caseiro, WFST Compression for Automatic Speech Recognition, in InterSpeech, D.Moore et al., Juicer: A Weighted Finite-State Transducer speech decoder, in MLMI, A.Lee et al., Recent Development of Open-Source Speech Recognition Engine Julius, in APSIPA, A.Serralheiro et al., Towards a Repository of Digital 2013 Information Processing Society of Japan [14] [15] [16] [17] Talking Books, in EuroSpeech, J.Schalkwyk et al., Speech Recognition with Dynamic Grammars Using Finite-State Transducers, in EuroSpeech, C.Allauzen et al, A Generalized Composition Algorithm for Weighted Finite-State Transducers, in InterSpeech, L.Mangu et al., Finding Consensus in Speech Recognition : Word Error Minimization and Other Applications of Confusion Networks, Computer Speech and Language, vol.14, no.4, pp , D.Hakkani-Tur et al., Beyond ASR 1-best: Using word confusion networks in spoken language understanding, Computer Speech and Language, vol.20, no.4, pp ,
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