Word graphs size impact on the performance of handwriting document applications

[EN] Two document processing applications are con- sidered: computer-assisted transcription of text images (CATTI) and Keyword Spotting (KWS), for transcribing and indexing handwritten documents, respectively. Instead of working directly on the handwriting images, both of them employ meta-data structures called word graphs (WG), which are obtained using segmentation-free hand- written text recognition technology based on N-gram lan- guage models and hidden Markov models. A WG contains most of the relevant information of the original text (line) image required by CATTI and KWS but, if it is too large, the computational cost of generating and using it can become unafordable. Conversely, if it is too small, relevant information may be lost, leading to a reduction of CATTI or KWS performance. We study the trade-off between WG size and performance in terms of effectiveness and effi- ciency of CATTI and KWS. Results show that small, computationally cheap WGs can be used without loosing the excellent CATTI and KWS performance achieved with huge WGs.Work partially supported by the Generalitat Valenciana under the Prometeo/2009/014 Project Grant ALMAMATER, by the Spanish MECD as part of the Valorization and I+D+I Resources program of VLC/CAMPUS in the International Excellence Campus program, and through the EU projects: HIMANIS (JPICH programme, Spanish Grant Ref. PCIN-2015-068) and READ (Horizon-2020 programme, Grant Ref. 674943).Toselli ., AH.; Romero Gómez, V.; Vidal, E. (2017). Word graphs size impact on the performance of handwriting document applications. Neural Computing and Applications. 28(9):2477-2487. https://doi.org/10.1007/s00521-016-2336-2S24772487289Amengual JC, Vidal E (1998) Efficient error-correcting Viterbi parsing. IEEE Trans Pattern Anal Mach Intell 20(10):1109–1116Bazzi I, Schwartz R, Makhoul J (1999) An omnifont open-vocabulary OCR system for English and Arabic. IEEE Trans Pattern Anal Mach Intell 21(6):495–504Erman L, Lesser V (1990) The HEARSAY-II speech understanding system: a tutorial. Readings in Speech Reasoning, pp 235–245Evermann G (1999) Minimum word error rate decoding. Ph.D. thesis, Churchill College, University of CambridgeFischer A, Wuthrich M, Liwicki M, Frinken V, Bunke H, Viehhauser G, Stolz M (2009) Automatic transcription of handwritten medieval documents. In: 15th international conference on virtual systems and multimedia, 2009. VSMM ’09, pp 137–142Frinken V, Fischer A, Manmatha R, Bunke H (2012) A novel word spotting method based on recurrent neural networks. IEEE Trans Pattern Anal Mach Intell 34(2):211–224Furcy D, Koenig S (2005) Limited discrepancy beam search. In: Proceedings of the 19th international joint conference on artificial intelligence, IJCAI’05, pp 125–131Granell E, Martínez-Hinarejos CD (2015) Multimodal output combination for transcribing historical handwritten documents. In: 16th international conference on computer analysis of images and patterns, CAIP 2015, chap, pp 246–260. Springer International PublishingHakkani-Tr D, Bchet F, Riccardi G, Tur G (2006) Beyond ASR 1-best: using word confusion networks in spoken language understanding. Comput Speech Lang 20(4):495–514Jelinek F (1998) Statistical methods for speech recognition. MIT Press, CambridgeJurafsky D, Martin JH (2009) Speech and language processing: an introduction to natural language processing, speech recognition, and computational linguistics, 2nd edn. Prentice-Hall, Englewood CliffsKneser R, Ney H (1995) Improved backing-off for N-gram language modeling. In: International conference on acoustics, speech and signal processing (ICASSP ’95), vol 1, pp 181–184. IEEE Computer SocietyLiu P, Soong FK (2006) Word graph based speech recognition error correction by handwriting input. In: Proceedings of the 8th international conference on multimodal interfaces, ICMI ’06, pp 339–346. ACMLowerre BT (1976) The harpy speech recognition system. Ph.D. thesis, Pittsburgh, PALuján-Mares M, Tamarit V, Alabau V, Martínez-Hinarejos CD, Pastor M, Sanchis A, Toselli A (2008) iATROS: a speech and handwritting recognition system. In: V Jornadas en Tecnologías del Habla (VJTH’2008), pp 75–78Mangu L, Brill E, Stolcke A (2000) Finding consensus in speech recognition: word error minimization and other applications of confusion networks. Comput Speech Lang 14(4):373–400Manning CD, Raghavan P, Schutze H (2008) Introduction to information retrieval. Cambridge University Press, New YorkMohri M, Pereira F, Riley M (2002) Weighted finite-state transducers in speech recognition. Comput Speech Lang 16(1):69–88Odell JJ, Valtchev V, Woodland PC, Young SJ (1994) A one pass decoder design for large vocabulary recognition. In: Proceedings of the workshop on human language technology, HLT ’94, pp 405–410. Association for Computational LinguisticsOerder M, Ney H (1993) Word graphs: an efficient interface between continuous-speech recognition and language understanding. IEEE Int Conf Acoust Speech Signal Process 2:119–122Olivie J, Christianson C, McCarry J (eds) (2011) Handbook of natural language processing and machine translation. Springer, BerlinOrtmanns S, Ney H, Aubert X (1997) A word graph algorithm for large vocabulary continuous speech recognition. Comput Speech Lang 11(1):43–72Padmanabhan M, Saon G, Zweig G (2000) Lattice-based unsupervised MLLR for speaker adaptation. In: ASR2000-automatic speech recognition: challenges for the New Millenium ISCA Tutorial and Research Workshop (ITRW)Pesch H, Hamdani M, Forster J, Ney H (2012) Analysis of preprocessing techniques for latin handwriting recognition. In: International conference on frontiers in handwriting recognition, ICFHR’12, pp 280–284Povey D, Ghoshal A, Boulianne G, Burget L, Glembek O, Goel N, Hannemann M, Motlicek P, Qian Y, Schwarz P, Silovsky J, Stemmer G, Vesely K (2011) The Kaldi speech recognition toolkit. In: IEEE 2011 workshop on automatic speech recognition and understanding. IEEE Signal Processing SocietyPovey D, Hannemann M, Boulianne G, Burget L, Ghoshal A, Janda M, Karafiat M, Kombrink S, Motlcek P, Qian Y, Riedhammer K, Vesely K, Vu NT (2012) Generating Exact Lattices in the WFST Framework. In: IEEE international conference on acoustics, speech, and signal processing (ICASSP)Rabiner L (1989) A tutorial of hidden Markov models and selected application in speech recognition. Proc IEEE 77:257–286Robertson S (2008) A new interpretation of average precision. In: Proceedings of the international ACM SIGIR conference on research and development in information retrieval (SIGIR ’08), pp 689–690. ACMRomero V, Toselli AH, Rodríguez L, Vidal E (2007) Computer assisted transcription for ancient text images. Proc Int Conf Image Anal Recogn LNCS 4633:1182–1193Romero V, Toselli AH, Vidal E (2012) Multimodal interactive handwritten text transcription. Series in machine perception and artificial intelligence (MPAI). World Scientific Publishing, SingaporeRybach D, Gollan C, Heigold G, Hoffmeister B, Lööf J, Schlüter R, Ney H (2009) The RWTH aachen university open source speech recognition system. In: Interspeech, pp 2111–2114Sánchez J, Mühlberger G, Gatos B, Schofield P, Depuydt K, Davis R, Vidal E, de Does J (2013) tranScriptorium: an European project on handwritten text recognition. In: DocEng, pp 227–228Saon G, Povey D, Zweig G (2005) Anatomy of an extremely fast LVCSR decoder. In: INTERSPEECH, pp 549–552Strom N (1995) Generation and minimization of word graphs in continuous speech recognition. In: Proceedings of IEEE workshop on ASR’95, pp 125–126. Snowbird, UtahTanha J, de Does J, Depuydt K (2015) Combining higher-order N-grams and intelligent sample selection to improve language modeling for Handwritten Text Recognition. In: ESANN 2015 proceedings, European symposium on artificial neural networks, computational intelligence and machine learning, pp 361–366Toselli A, Romero V, i Gadea MP, Vidal E (2010) Multimodal interactive transcription of text images. Pattern Recogn 43(5):1814–1825Toselli A, Romero V, Vidal E (2015) Word-graph based applications for handwriting documents: impact of word-graph size on their performances. In: Paredes R, Cardoso JS, Pardo XM (eds) Pattern recognition and image analysis. Lecture Notes in Computer Science, vol 9117, pp 253–261. Springer International PublishingToselli AH, Juan A, Keysers D, Gonzlez J, Salvador I, Ney H, Vidal E, Casacuberta F (2004) Integrated handwriting recognition and interpretation using finite-state models. Int J Pattern Recogn Artif Intell 18(4):519–539Toselli AH, Vidal E (2013) Fast HMM-Filler approach for key word spotting in handwritten documents. In: Proceedings of the 12th international conference on document analysis and recognition (ICDAR’13). IEEE Computer SocietyToselli AH, Vidal E, Romero V, Frinken V (2013) Word-graph based keyword spotting and indexing of handwritten document images. Technical report, Universitat Politècnica de ValènciaUeffing N, Ney H (2007) Word-level confidence estimation for machine translation. Comput Linguist 33(1):9–40. doi: 10.1162/coli.2007.33.1.9Vinciarelli A, Bengio S, Bunke H (2004) Off-line recognition of unconstrained handwritten texts using HMMs and statistical language models. IEEE Trans Pattern Anal Mach Intell 26(6):709–720Weng F, Stolcke A, Sankar A (1998) Efficient lattice representation and generation. In: Proceedings of ICSLP, pp 2531–2534Wessel F, Schluter R, Macherey K, Ney H (2001) Confidence measures for large vocabulary continuous speech recognition. IEEE Trans Speech Audio Process 9(3):288–298Wolf J, Woods W (1977) The HWIM speech understanding system. In: IEEE international conference on acoustics, speech, and signal processing, ICASSP ’77, vol 2, pp 784–787Woodland P, Leggetter C, Odell J, Valtchev V, Young S (1995) The 1994 HTK large vocabulary speech recognition system. In: International conference on acoustics, speech, and signal processing (ICASSP ’95), vol 1, pp 73 –76Young S, Odell J, Ollason D, Valtchev V, Woodland P (1997) The HTK book: hidden Markov models toolkit V2.1. Cambridge Research Laboratory Ltd, CambridgeYoung S, Russell N, Thornton J (1989) Token passing: a simple conceptual model for connected speech recognition systems. Technical reportZhu M (2004) Recall, precision and average precision. Working Paper 2004–09 Department of Statistics and Actuarial Science, University of WaterlooZimmermann M, Bunke H (2004) Optimizing the integration of a statistical language model in hmm based offline handwritten text recognition. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, vol 2, pp 541–54

Design and implementation of a user-oriented speech recognition interface: the synergy of technology and human factors

The design and implementation of a user-oriented speech recognition interface are described. The interface enables the use of speech recognition in so-called interactive voice response systems which can be accessed via a telephone connection. In the design of the interface a synergy of technology and human factors is achieved. This synergy is very important for making speech interfaces a natural and acceptable form of human-machine interaction. Important concepts such as interfaces, human factors and speech recognition are discussed. Additionally, an indication is given as to how the synergy of human factors and technology can be realised by a sketch of the interface's implementation. An explanation is also provided of how the interface might be integrated in different applications fruitfully

The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings

We motivate and describe a new freely available human-human dialogue dataset for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; Mills and Healey, submitted) with a novel task, where a Learner needs to learn invented visual attribute words (such as " burchak " for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include self-and other-correction, mid-sentence continuations, interruptions, overlaps, fillers, and hedges. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78% turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rule-based system built previously.Comment: 10 pages, THE 6TH WORKSHOP ON VISION AND LANGUAGE (VL'17

Integrated speech and morphological processing in a connectionist continuous speech understanding for Korean

A new tightly coupled speech and natural language integration model is presented for a TDNN-based continuous possibly large vocabulary speech recognition system for Korean. Unlike popular n-best techniques developed for integrating mainly HMM-based speech recognition and natural language processing in a {\em word level}, which is obviously inadequate for morphologically complex agglutinative languages, our model constructs a spoken language system based on a {\em morpheme-level} speech and language integration. With this integration scheme, the spoken Korean processing engine (SKOPE) is designed and implemented using a TDNN-based diphone recognition module integrated with a Viterbi-based lexical decoding and symbolic phonological/morphological co-analysis. Our experiment results show that the speaker-dependent continuous {\em eojeol} (Korean word) recognition and integrated morphological analysis can be achieved with over 80.6% success rate directly from speech inputs for the middle-level vocabularies.Comment: latex source with a4 style, 15 pages, to be published in computer processing of oriental language journa

Infants segment words from songs - an EEG study

Children’s songs are omnipresent and highly attractive stimuli in infants’ input. Previous work suggests that infants process linguistic–phonetic information from simplified sung melodies. The present study investigated whether infants learn words from ecologically valid children’s songs. Testing 40 Dutch-learning 10-month-olds in a familiarization-then-test electroencephalography (EEG) paradigm, this study asked whether infants can segment repeated target words embedded in songs during familiarization and subsequently recognize those words in continuous speech in the test phase. To replicate previous speech work and compare segmentation across modalities, infants participated in both song and speech sessions. Results showed a positive event-related potential (ERP) familiarity effect to the final compared to the first target occurrences during both song and speech familiarization. No evidence was found for word recognition in the test phase following either song or speech. Comparisons across the stimuli of the present and a comparable previous study suggested that acoustic prominence and speech rate may have contributed to the polarity of the ERP familiarity effect and its absence in the test phase. Overall, the present study provides evidence that 10-month-old infants can segment words embedded in songs, and it raises questions about the acoustic and other factors that enable or hinder infant word segmentation from songs and speech