Sélection de données textuelles pour la modélisation du langage dans le cadre de la reconnaissance vocale automatique
##plugins.themes.bootstrap3.article.sidebar##
منشور:
May 30, 2016
الكلمات المفتاحية:
عملية اختيار البيانات – النماذج اللغوية – نسخ الكلام
##plugins.themes.bootstrap3.article.main##
الملخص
يعد نموذج اللغة وحدة مهمة في العديد من التطبيقات التي تنتج نصًا باللغة الطبيعية، وخاصة التعرف على الكلام. يتطلب تدريب النماذج اللغوية كميات كبيرة من البيانات النصية التي تتوافق مع المجال المستهدف. لقد تم التحقيق في اختيار بيانات المجال المستهدف (أو داخل المجال) في الماضي. على سبيل المثال، اقترح [1] معيارًا يعتمد على اختلاف الإنتروبيا المتقاطعة بين النماذج التي تمثل البيانات داخل المجال والبيانات غير الخاصة بالمجال. ومع ذلك، تم إجراء التقييمات باستخدام مصدرين فقط للبيانات، أحدهما يتوافق مع المجال الداخلي والآخر للبيانات العامة التي يتم اختيار الجمل منها. في نطاق أنظمة نسخ الأخبار والبرامج التلفزيونية، يتم بناء نماذج اللغة عن طريق استيفاء العديد من نماذج اللغة المقدرة من مصادر البيانات المختلفة. تبحث هذه الورقة في عملية اختيار البيانات في هذا السياق لبناء نماذج لغوية محرفة لنسخ الكلام. تظهر النتائج أنه، في عملية الاختيار، يعد اختيار نماذج اللغة لتمثيل البيانات داخل المجال والبيانات غير الخاصة بالمجال أمرًا بالغ الأهمية. علاوة على ذلك، فمن الأفضل تطبيق اختيار البيانات فقط على بعض مصادر البيانات المحددة. بهذه الطريقة، تؤدي عملية الاختيار إلى تحسن قدره 8.3 من حيث الحيرة و0.2% من حيث معدل خطأ الكلمات في مهمة النسخ الإذاعي الفرنسي.
##plugins.themes.bootstrap3.article.details##
كيفية الاقتباس
مزوجف., لونقواد., & جوفيد. (2016). Sélection de données textuelles pour la modélisation du langage dans le cadre de la reconnaissance vocale automatique. AL-Lisaniyyat, 22(2), 28-33. https://doi.org/10.61850/allj.v22i2.370
القسم
Articles
المراجع
P.Swietojanski,A.Ghosal, and S.Renals,Convolutional neural networks for distan speech recognition, IEEE Signal processing letters , vol.21,no.9,pp.1120-1124,sept2014.
N.Jaitly,P.Nguyen,A.SENIOR,and V.Vanhoucke, Application of pretrained deep neural networks to large vocabulary speech recognition,in proceedings of interspeech,2012.
E.S.Ristad and P.N. Yianilos ,Learning string-edit distance,Pattern analysis and machine intelligence ,IEEE Transaction on , vol.20,no.5,pp.5222-532,1998.
A.C.Morris, V. Maier ,and P.Green, From wer and ril to mer and wil: improved evaluation measures for connected speech recognition.in INTERSPEECH,2004.
I.A.McCowan ,D.Moore, J.Dines , D.Gatica-perez,M.Flynn,p.Wellner, and H.Bourlard , On the use of information retrieval measures for speech recognition evaluation, IDIAP, Tech.rep., 2004.
I.A.Mcowan,D.Moore , J.Dines,D.Gatica-perez,M.Flynn,P.Wellner, and H.Bourlard, On the use of information retrieval measures for speech recognition evaluating automated speech recognition devices and the consequences of using probabilistic string edit distance as input , 3rd year project,Sheffield university ,2002.
J.Hoffman ,Papoulis ,a-probability random variables and stochastic processes,1967.
H.Nanjo and T.Kawahara.A new asr evaluation measure and minimum bayes-risk decoding for open-domain speech understanding .in in the proceeding of the IEEE International conference on acoustics, speech, and signal processing ICASSP ,2005,pp.1053-1056.
B.Favre,K.Cheung ,S .Kazemian,A.Lee, Y.Liu, C.Munteanu, A.Nenkova,D.Ochei,G.Penn,S.Tratz et al.,Automatic human utility evaluation of asr systems: does wer really predict performance in Interspeech,2013,pp.3463-3467.
H.Jiang,Confidence measures for speech recognition:A Survey, speech communication,vol 45,no .4pp.466-470,2005.
L.Zhou,Y.Shi,JFeng, and A.Sears,Data mining for detecting erroes in dictation speech recognition,speech and audio processing,IEEE Transactions on ,vol.13,no.5,pp.681-688,2005.
A.Allauzen ,Error detection in confusion network .in INTERSPEECH?2007,pp.1749-1752.
T.Pellegrini and I.Trancoso,ERROR detection in broadcast news asr using markov chains ,in human language technology.Challenges for computer science and linguistics.Springer ,2011,pp.59-69.
W.Chen ,S. Ananthakrishnan ,R.Kumar ,R.Prasad ,and P.Natarajan, ASR error detection in a conversational spoken language translation system ,in the proceedings of the IEEE International conference on acoustics ,speech and signal processing ICASSP. IEEE,2013,pp.7418-7422.
T.Pellegrini and I.Trancoso,Improving asr error detection with nondecoder based features.in Interspeech,2010,pp.1950-1953.
W.A.Ainsworth and S.Pratt,Feedbach strategies for error correction in speech recognition systems,International journal of man-machine studies,vol.36,no.6,pp.833-842,1992.
A.Murray,C.Frankish ,and D.jones , data-entry by voice :Facilitating correction of misrecognitions, in interactive speech technology.Taylor and francis , INc., 1993,pp.137-144.
B.Suhm,B. Myers, and A.Waibel ,Multimodal error correction for speech user interfaces ,ACM transactions on computer-human interaction TOCHI , vol.8,no.1.pp.60-98,2001.
J.Feng and A.Sears, Using confidences scores to improve handsfree speech based navigation in continuous dictation. Systems, ACM transactions on computer-human interaction Tochi ,vol.11,no.4,pp.329-356,2004.
D.YU?M.-y.H wang, P.Mau,A.ACERO, and L.Deng, Unsupervised learning from users error correction in speech dictation .in interspeech,2004.
Y.Shi and L.ZHOU,Supporting dictation supporting dictation speech recognition erroe correction : the impact of external information, Behaviour and information technology,vol.30.no.6,pp.961-774,2011.*
A.Sarma and D.D.Palmer.Context-based speech recognition error detection and correction, in proceedings of HLT-NAACL 2004,pp.85-88.
Y.Bassil and P.Semaan, Asr context-sensitive error correction based on Microsoft n-gram dataset,arxiv preprint arxiv:1203.5262,2012.
N.Jaitly,P.Nguyen,A.SENIOR,and V.Vanhoucke, Application of pretrained deep neural networks to large vocabulary speech recognition,in proceedings of interspeech,2012.
E.S.Ristad and P.N. Yianilos ,Learning string-edit distance,Pattern analysis and machine intelligence ,IEEE Transaction on , vol.20,no.5,pp.5222-532,1998.
A.C.Morris, V. Maier ,and P.Green, From wer and ril to mer and wil: improved evaluation measures for connected speech recognition.in INTERSPEECH,2004.
I.A.McCowan ,D.Moore, J.Dines , D.Gatica-perez,M.Flynn,p.Wellner, and H.Bourlard , On the use of information retrieval measures for speech recognition evaluation, IDIAP, Tech.rep., 2004.
I.A.Mcowan,D.Moore , J.Dines,D.Gatica-perez,M.Flynn,P.Wellner, and H.Bourlard, On the use of information retrieval measures for speech recognition evaluating automated speech recognition devices and the consequences of using probabilistic string edit distance as input , 3rd year project,Sheffield university ,2002.
J.Hoffman ,Papoulis ,a-probability random variables and stochastic processes,1967.
H.Nanjo and T.Kawahara.A new asr evaluation measure and minimum bayes-risk decoding for open-domain speech understanding .in in the proceeding of the IEEE International conference on acoustics, speech, and signal processing ICASSP ,2005,pp.1053-1056.
B.Favre,K.Cheung ,S .Kazemian,A.Lee, Y.Liu, C.Munteanu, A.Nenkova,D.Ochei,G.Penn,S.Tratz et al.,Automatic human utility evaluation of asr systems: does wer really predict performance in Interspeech,2013,pp.3463-3467.
H.Jiang,Confidence measures for speech recognition:A Survey, speech communication,vol 45,no .4pp.466-470,2005.
L.Zhou,Y.Shi,JFeng, and A.Sears,Data mining for detecting erroes in dictation speech recognition,speech and audio processing,IEEE Transactions on ,vol.13,no.5,pp.681-688,2005.
A.Allauzen ,Error detection in confusion network .in INTERSPEECH?2007,pp.1749-1752.
T.Pellegrini and I.Trancoso,ERROR detection in broadcast news asr using markov chains ,in human language technology.Challenges for computer science and linguistics.Springer ,2011,pp.59-69.
W.Chen ,S. Ananthakrishnan ,R.Kumar ,R.Prasad ,and P.Natarajan, ASR error detection in a conversational spoken language translation system ,in the proceedings of the IEEE International conference on acoustics ,speech and signal processing ICASSP. IEEE,2013,pp.7418-7422.
T.Pellegrini and I.Trancoso,Improving asr error detection with nondecoder based features.in Interspeech,2010,pp.1950-1953.
W.A.Ainsworth and S.Pratt,Feedbach strategies for error correction in speech recognition systems,International journal of man-machine studies,vol.36,no.6,pp.833-842,1992.
A.Murray,C.Frankish ,and D.jones , data-entry by voice :Facilitating correction of misrecognitions, in interactive speech technology.Taylor and francis , INc., 1993,pp.137-144.
B.Suhm,B. Myers, and A.Waibel ,Multimodal error correction for speech user interfaces ,ACM transactions on computer-human interaction TOCHI , vol.8,no.1.pp.60-98,2001.
J.Feng and A.Sears, Using confidences scores to improve handsfree speech based navigation in continuous dictation. Systems, ACM transactions on computer-human interaction Tochi ,vol.11,no.4,pp.329-356,2004.
D.YU?M.-y.H wang, P.Mau,A.ACERO, and L.Deng, Unsupervised learning from users error correction in speech dictation .in interspeech,2004.
Y.Shi and L.ZHOU,Supporting dictation supporting dictation speech recognition erroe correction : the impact of external information, Behaviour and information technology,vol.30.no.6,pp.961-774,2011.*
A.Sarma and D.D.Palmer.Context-based speech recognition error detection and correction, in proceedings of HLT-NAACL 2004,pp.85-88.
Y.Bassil and P.Semaan, Asr context-sensitive error correction based on Microsoft n-gram dataset,arxiv preprint arxiv:1203.5262,2012.