Oesophageal speech: enrichment and evaluations

167 p.After a laryngectomy (i.e. removal of the larynx) a patient can no more speak in a healthy laryngeal voice. Therefore, they need to adopt alternative methods of speaking such as oesophageal speech. In this method, speech is produced using swallowed air and the vibrations of the pharyngo-oesophageal segment, which introduces several undesired artefacts and an abnormal fundamental frequency. This makes oesophageal speech processing difficult compared to healthy speech, both auditory processing and signal processing. The aim of this thesis is to find solutions to make oesophageal speech signals easier to process, and to evaluate these solutions by exploring a wide range of evaluation metrics.First, some preliminary studies were performed to compare oesophageal speech and healthy speech. This revealed significantly lower intelligibility and higher listening effort for oesophageal speech compared to healthy speech. Intelligibility scores were comparable for familiar and non-familiar listeners of oesophageal speech. However, listeners familiar with oesophageal speech reported less effort compared to non-familiar listeners. In another experiment, oesophageal speech was reported to have more listening effort compared to healthy speech even though its intelligibility was comparable to healthy speech. On investigating neural correlates of listening effort (i.e. alpha power) using electroencephalography, a higher alpha power was observed for oesophageal speech compared to healthy speech, indicating higher listening effort. Additionally, participants with poorer cognitive abilities (i.e. working memory capacity) showed higher alpha power.Next, using several algorithms (preexisting as well as novel approaches), oesophageal speech was transformed with the aim of making it more intelligible and less effortful. The novel approach consisted of a deep neural network based voice conversion system where the source was oesophageal speech and the target was synthetic speech matched in duration with the source oesophageal speech. This helped in eliminating the source-target alignment process which is particularly prone to errors for disordered speech such as oesophageal speech. Both speaker dependent and speaker independent versions of this system were implemented. The outputs of the speaker dependent system had better short term objective intelligibility scores, automatic speech recognition performance and listener preference scores compared to unprocessed oesophageal speech. The speaker independent system had improvement in short term objective intelligibility scores but not in automatic speech recognition performance. Some other signal transformations were also performed to enhance oesophageal speech. These included removal of undesired artefacts and methods to improve fundamental frequency. Out of these methods, only removal of undesired silences had success to some degree (1.44 \% points improvement in automatic speech recognition performance), and that too only for low intelligibility oesophageal speech.Lastly, the output of these transformations were evaluated and compared with previous systems using an ensemble of evaluation metrics such as short term objective intelligibility, automatic speech recognition, subjective listening tests and neural measures obtained using electroencephalography. Results reveal that the proposed neural network based system outperformed previous systems in improving the objective intelligibility and automatic speech recognition performance of oesophageal speech. In the case of subjective evaluations, the results were mixed - some positive improvement in preference scores and no improvement in speech intelligibility and listening effort scores. Overall, the results demonstrate several possibilities and new paths to enrich oesophageal speech using modern machine learning algorithms. The outcomes would be beneficial to the disordered speech community

Word-Graph Based Applications for Handwriting Documents: Impact of Word-Graph Size on Their Performances

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8 29Computer Assisted Transcription of Text Images (CATTI) and Key-Word Spotting (KWS) applications aim at transcribing and indexing handwritten documents respectively. They both are approached by means of Word Graphs (WG) obtained using segmentation-free handwritten text recognition technology based on N-gram Language Models and Hidden Markov Models. A large WG contains most of the relevant information of the original text (line) image needed for CATTI and KWS but, if it is too large, the computational cost of generating and using it can become unaffordable. Conversely, if it is too small, relevant information may be lost, leading to a reduction of CATTI/KWS in performance accuracy. We study the trade-off between WG size and CATTI &KWS performance in terms of effectiveness and efficiency. Results show that small, computationally cheap WGs can be used without loosing the excellent CATTI/KWS performance achieved with huge WGs.Work partially supported by the Spanish MICINN projects STraDA (TIN2012-37475-C02-01) and by the EU 7th FP tranScriptorium project (Ref:600707).Toselli, AH.; Romero Gómez, V.; Vidal Ruiz, E. (2015). Word-Graph Based Applications for Handwriting Documents: Impact of Word-Graph Size on Their Performances. En Pattern Recognition and Image Analysis. Springer. 253-261. https://doi.org/10.1007/978-3-319-19390-8_29S253261Romero, V., Toselli, A.H., Vidal, E.: Multimodal Interactive Handwritten Text Transcription. Series in Machine Perception and Artificial Intelligence (MPAI). World Scientific Publishing, Singapore (2012)Toselli, A.H., Vidal, E., Romero, V., Frinken, V.: Word-graph based keyword spotting and indexing of handwritten document images. Technical report, Universitat Politècnica de València (2013)Oerder, M., Ney, H.: Word graphs: an efficient interface between continuous-speech recognition and language understanding. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 119–122, April 1993Bazzi, I., Schwartz, R., Makhoul, J.: An omnifont open-vocabulary OCR system for English and Arabic. IEEE Trans. Pattern Anal. Mach. Intell. 21(6), 495–504 (1999)Jelinek, F.: Statistical Methods for Speech Recognition. MIT Press, Cambridge (1998)Ström, N.: Generation and minimization of word graphs in continuous speech recognition. In: Proceedings of IEEE Workshop on ASR 1995, Snowbird, Utah, pp. 125–126 (1995)Ortmanns, S., Ney, H., Aubert, X.: A word graph algorithm for large vocabulary continuous speech recognition. Comput. Speech Lang. 11(1), 43–72 (1997)Wessel, F., Schluter, R., Macherey, K., Ney, H.: Confidence measures for large vocabulary continuous speech recognition. IEEE Trans. Speech Audio Process. 9(3), 288–298 (2001)Robertson, S.: A new interpretation of average precision. In: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), pp. 689–690. ACM, USA (2008)Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press, USA (2008)Romero, V., Toselli, A.H., Rodríguez, L., Vidal, E.: Computer assisted transcription for ancient text images. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 1182–1193. Springer, Heidelberg (2007)Fischer, A., Wuthrich, M., Liwicki, M., Frinken, V., Bunke, H., Viehhauser, G., Stolz, M.: Automatic transcription of handwritten medieval documents. In: 15th International Conference on Virtual Systems and Multimedia, VSMM 2009, pp. 137–142 (2009)Pesch, H., Hamdani, M., Forster, J., Ney, H.: Analysis of preprocessing techniques for latin handwriting recognition. In: ICFHR, pp. 280–284 (2012)Evermann, G.: Minimum Word Error Rate Decoding. Ph.D. thesis, Churchill College, University of Cambridge (1999

Ensemble acoustic modeling in automatic speech recognition

Title from PDF of title page (University of Missouri--Columbia, viewed on May 25, 2012).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Yunxin ZhaoVita.Ph. D. University of Missouri--Columbia 2011"December 2011"In this dissertation, several new approaches of using data sampling to construct an Ensemble of Acoustic Models (EAM) for speech recognition are proposed. A straightforward method of data sampling is Cross Validation (CV) data partition. In the direction of improving inter-model diversity within an EAM for speaker independent speech recognition, we propose Speaker Clustering (SC) based data sampling. In the direction of improving base model quality as well as inter-model diversity, we further investigate the effects of several successful techniques of single model training in speech recognition on the proposed ensemble acoustic models, including Cross Validation Expectation Maximization (CVEM), Discriminative Training (DT), and Multiple Layer Perceptron (MLP) features. We have evaluated the proposed methods on TIMIT phoneme recognition task as well as on a telemedicine automatic captioning task. The proposed EAMs have led to significant improvements in recognition accuracy over conventional Hidden Markov Model (HMM) baseline systems, and the integration of EAM with CVEM, DT and MLP has also significantly improved the accuracy performances of CVEM, DT, and MLP based single model systems. We further investigated the largely unstudied factor of inter-model diversity, and proposed several methods to explicit measure inter-model diversity. We demonstrate a positive relation between enlarging inter-model diversity and increasing EAM quality. Compacting the acoustic model to a reasonable size for practical applications while maintaining a reasonable performance is needed for EAM. Toward this goal, in this dissertation, we discuss and investigate several distance measures and proposed global optimization algorithms for clustering methods. We also proposed an explicit PDT (EPDT) state tying approach that allows Phoneme data Sharing (PS) for its potential capability in accommodating pronunciation variations.Includes bibliographical reference

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

Towards a tool for the subjective assessment of speech system interfaces (SASSI)

Applications of speech recognition are now widespread, but user-centred evaluation methods are necessary to ensure their success. Objective evaluation techniques are fairly well established, but previous subjective techniques have been unstructured and unproven. This paper reports on the first stage of the development of a questionnaire measure for the Subjective Assessment of Speech System Interfaces (SASSI). The aim of the research programme is to produce a valid, reliable and sensitive measure of users' subjective experiences with speech recognition systems. Such a technique could make an important contribution to theory and practice in the design and evaluation of speech recognition systems according to best human factors practice. A prototype questionnaire was designed, based on established measures for evaluating the usability of other kinds of user interface, and on a review of the research literature into speech system design. This consisted of 50 statements with which respondents rated their level of agreement. The questionnaire was given to users of four different speech applications, and Exploratory Factor Analysis of 214 completed questionnaires was conducted. This suggested the presence of six main factors in users' perceptions of speech systems: System Response Accuracy, Likeability, Cognitive Demand, Annoyance, Habitability and Speed. The six factors have face validity, and a reasonable level of statistical reliability. The findings form a userful theoretical and practical basis for the subjective evaluation of any speech recognition interface. However, further work is recommended, to establish the validity and sensitivity of the approach, before a final tool can be produced which warrants general use

ASR error management for improving spoken language understanding

This paper addresses the problem of automatic speech recognition (ASR) error detection and their use for improving spoken language understanding (SLU) systems. In this study, the SLU task consists in automatically extracting, from ASR transcriptions , semantic concepts and concept/values pairs in a e.g touristic information system. An approach is proposed for enriching the set of semantic labels with error specific labels and by using a recently proposed neural approach based on word embeddings to compute well calibrated ASR confidence measures. Experimental results are reported showing that it is possible to decrease significantly the Concept/Value Error Rate with a state of the art system, outperforming previously published results performance on the same experimental data. It also shown that combining an SLU approach based on conditional random fields with a neural encoder/decoder attention based architecture , it is possible to effectively identifying confidence islands and uncertain semantic output segments useful for deciding appropriate error handling actions by the dialogue manager strategy .Comment: Interspeech 2017, Aug 2017, Stockholm, Sweden. 201