9 accepted papers
How important are different temporal speech modulations for speech recognition? We answer this question from two complementary perspectives. Firstly, we quantify the amount of phonetic information in the modulation spectrum of speech by computing the mutual information between temporal modulations w…
The multi-stream paradigm of audio processing, in which several sources are simultaneously considered, has been an active research area for information fusion. Our previous study offered a promising direction within end-to-end automatic speech recognition, where parallel encoders aim to capture dive…
Human hearing and human speech are intrinsically tied together, as the properties of speech almost certainly developed in order to be heard by human ears. As a result of this connection, it has been shown that certain properties of human hearing are mimicked within data-driven systems that are train…
In this paper, we propose a novel method to capture energy modulations from different frequency bands in speech into frame-level feature vectors, called Modulation-vectors or M-vectors, for use in Automatic Speech Recognition (ASR) systems. We show that in different multi-stream setups, with paralle…
Automatic Speech Recognition (ASR) using multiple microphone arrays has achieved great success in the far-field robustness. Taking advantage of all the information that each array shares and contributes is crucial in this task. Motivated by the advances of joint Connectionist Temporal Classification…
This work explores different methods to detect errors in transcriptions of speech recordings. We artificially corrupt well transcribed speech transcriptions with three types of errors: substitution, insertion and deletion on TIMIT phonemic transcriptions and WSJ word transcriptions. First, we use Ba…
In this paper we investigate methods to predict word error rates in automatic speech recognition in the presence of unknown noise types, which have not been seen during training. The performance measures operate on phoneme posteriorgrams that are obtained from neural nets. We compare average frame-w…
Robustness of automatic speech recognition (ASR) to acoustic mismatches can be improved by multistream framework. Frequently used approach to combine decisions from individual streams involve training large number of neural networks, one for each possible stream combination. In this work, we propose…
A group of junior and senior researchers gathered as a part of the 2014 Frederick Jelinek Memorial Workshop in Prague to address the problem of predicting the accuracy of a nonlinear Deep Neural Network probability estimator for unknown data in a different application domain from the domain in which…