Effect of articulatory and acoustic features on the intelligibility of speech in noise: an articulatory synthesis study Abstract   In noisy conditions, speakers involuntarily change their manner of speaking to enhance the intelligibility of their voices. The increased intelligibility of this socalled Lombard speech is enabled by the change of multiple articulatory and acoustic features. While the major features of Lombard speech are well known from previous studies, little is known about their relative contributions to the intelligibility of speech in noise. This study used an analysisbysynthesis strategy to explore the contributions of multiple of these features. To this end, an articulatory speech synthesizer was used to synthesize the ten German digit words xe2x80x9cNullxe2x80x9d to xe2x80x9cNeunxe2x80x9d, for all 16 combinations of four binary features, i.e., modal vs.xc2xa0pressed phonation, normal vs.xc2xa0increased F1 and F2 formant frequencies, normal vs.xc2xa0increased f0 mean and range, and normal vs.xc2xa0increased duration of vowels. Subjects were asked to try to recognize the synthesized words in the presence of strong pink noise and babble noise. Compared to xe2x80x9cplainxe2x80x9d speech, the word recognition rate was most improved by pressed phonation, followed by an increased f0 mean and f0 range, and increased formant frequencies. Increased duration of vowels slightly reduced the recognition rate for pink noise but had no effect for babble noise.

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.