TextitLoud!: RealTime Captioning and Transcribing App for Inclusive TeachingLearning of Hearing Impaired These days variety of audiovisual presentation tools are used for teaching the millennial learners. Although such tools improve the active participation of students in teachinglearning, these are least useful in scenarios where hearing impaired students are also part of the same class. Thus, these students may be left out from rest of the class if teachinglearning tools are not made accessible for them. In this work we propose an Android application TextitLoud! that renders speakeru0027s verbal content as text, in realtime along with the audiovisuals. Although, there are variety of tools available for speechtotext conversion, our application is novel in the sense that it displays the speakeru0027s voice as caption text in the foreground of any application. e.g. In a presentation the verbal contents of speaker will popup on top of slide contents.

An Investigation into the Effectiveness of Enhancement in ASR Training and Test for Chime5 Dinner Party Transcription Despite the strong modeling power of neural network acoustic models, speech enhancement has been shown to deliver additional word error rate improvements if multichannel data is available. However, there has been a longstanding debate whether enhancement should also be carried out on the ASR training data. In an extensive experimental evaluation on the acoustically very challenging CHiME5 dinner party data we show that: (i) cleaning up the training data can lead to substantial error rate reductions, and (ii) enhancement in training is advisable as long as enhancement in test is at least as strong as in training. This approach stands in contrast and delivers larger gains than the common strategy reported in the literature to augment the training database with additional artificially degraded speech. Together with an acoustic model topology consisting of initial CNN layers followed by factorized TDNN layers we achieve with 41.6 % and 43.2 % WER on the DEV and EVAL test sets, respectively, a new singlesystem stateoftheart result on the CHiME5 data. This is a 8 % relative improvement compared to the best word error rate published so far for a speech recognizer without system combination.