It was shown in (Raikar et al., 2020) that the measurement error in the microphone position affected the room impulse response (RIR) which in turn affected the single channel speech recognition. In this paper, we ex-tend this to study the more complex and realistic scenario of multi channel distant speech recognition. Specifically we simulate m speakers in a given room with n microphones speaking without overlap. Then channel audio is beamformed and passed through a speech to text (s2t) engine. We compare the s2t accuracy when the microphone locations are known exactly (ground truth) with the s2t accuracy when there is a measurement error in the location of the microphone. We report the performance of an end-to-end s2t on beamformed input in terms of character error rate (CER) and and also speech intelligibility and quality in terms of STOI and PESQ respectively.

Multimodal sentiment classification in practical applications may have to rely on erroneous and imperfect views, namely (a) language transcription from a speech recognizer and (b) under-performing acoustic views. This work focuses on improving the representations of these views by performing a deep canonical correlation analysis with the representations of the better performing manual transcription view. Enhanced representations of the imperfect views can be obtained even in absence of the perfect views and give an improved performance during test conditions. Evaluations on the CMU-MOSI and CMU-MOSEI datasets demonstrate the effectiveness of the proposed approach.