Phoneme Sequence Extraction of Learnerxe2x80x99s Pronunciation Errors for Foreign Language Learning This paper presents a method for extracting phoneme sequences in foreign language pronunciation learning that a learner is likely to mispronounce. Some conventional methods show a learnerxe2x80x99s phoneme errors using speech recognition technology. Those systems simply show only one phoneme error that the learner makes; they show no analytical results of phoneme errors that arise from previous or later phoneme sequences. A feature of the proposed method is the ability to extract a phoneme that a learner is likely to mispronounce as an ngram sequence rather than a single phoneme. We confirm the feasibility of our method with several experiments using a prototype of our learning system.

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.