Japanese speech intelligibility estimation and prediction using objective intelligibility indices under noisy and reverberant conditions

French, 1947, Factors governing the intelligibility of speech sounds, J Acoust Soc Am, 19, 90, 10.1121/1.1916407 Kryter, 1962, Methods for the calculation and use of the articulation index, J Acoust Soc Am, 34, 1689, 10.1121/1.1909094 American National Standards Institute, N.Y., USA, ANSI S3.5, American National Standard Methods for the Calculation of the Articulation Index (1969). American National Standards Institute, N.Y., USA, ANSI S3.5, Methods for the Calculation of the Speech Intelligibility Index (1995). Steeneken, 1980, A physical method for measuring speech-transmission quality, J Acoust Soc Am, 67, 318, 10.1121/1.384464 International Electrotechnical Commission, Geneva, Switzerland, IEC 60268-16, Sound system equipment-Part 16: Objective rating of speech intelligibility by speech transmission index (2003). Ma, 2009, Objective measures for predicting speech intelligibility in noisy conditions based on new band-importance functions, J Acoust Soc Am, 125, 3387, 10.1121/1.3097493 Taal, 2011, An algorithm for intelligibility prediction of time-frequency weighted noisy speech, IEEE Trans Audio, Speech, Lang Process, 19, 2125, 10.1109/TASL.2011.2114881 Jensen, 2014, Speech intelligibility prediction based on mutual information, IEEE/ACM Trans Audio, Speech, Lang Process, 22, 430, 10.1109/TASLP.2013.2295914 Jensen, 2016, An algorithm for predicting the intelligibility of speech masked by modulated noise maskers, IEEE/ACM Trans Audio, Speech, Language Process, 24, 2009, 10.1109/TASLP.2016.2585878 Cover, 2006 Li, 2013, Comparative investigation of objective speech intelligibility prediction measures for noise-reduced signals in Mandarin and Japanese, Proc. Interspeech, 1184 Kondo, 2013, Estimation of speech intelligibility using objective measures, Appl Acoust, 74, 63, 10.1016/j.apacoust.2012.06.009 Sakano, 2015, A Speech Intelligibility Estimation Method Using a Non-reference Feature Set, IEICE Trans Inform Syst, 98-D, 21, 10.1587/transinf.2014MUP0004 Nakazawa, 2018, De-reverberation using DNN for non-reference reverberant speech intelligibility estimation, 2378 Fukumori, 2011, Censrec-4: An evaluation framework for distant-talking speech recognition in reverberant environments, Acoustical Science and Technology, 32, 201, 10.1250/ast.32.201 Sakamoto, 2006, New 20-word lists for word intelligibility test in japanese, 2158 Yun, 2018, A deep learning-based approach to non-intrusive objective speech intelligibility estimation, IEICE Trans. Inform. Syst., E101.D, 1207, 10.1587/transinf.2017EDL8225 Koizumi, 2018, Dnn-based source enhancement to increase objective sound quality assessment score, IEEE/ACM Trans Audio, Speech, Lang Process, 26, 1780, 10.1109/TASLP.2018.2842156 Andersen, 2018, Refinement and validation of the binaural short time objective intelligibility measure for spatially diverse conditions, Speech Commun, 102, 1, 10.1016/j.specom.2018.06.001 Amano, 1999, Familiarity effect on spoken word recognition in japanese, vol. 2, 873 Suzuki, 1995, An optimum computer generated pulse signal suitable for the measurement of very long impulse responses, J Acoust Soc Am, 97, 1119, 10.1121/1.412224