9 accepted papers
End-to-end speech recognition is a promising technology for enabling compact automatic speech recognition (ASR) systems since it can unify the acoustic and language model into a single neural network. However, as a drawback, training of end-to-end speech recognizers always requires transcribed utter…
This paper proposes methods to improve a commonly used end-to-end speech recognition model, Listen-Attend-Spell (LAS). The methods we propose use multi-task learning to improve generalization of the model by leveraging information from multiple labels. The focus in this paper is on multi-task models…
Sequence-to-sequence models provide a simple and elegant solution for building speech recognition systems by folding separate components of a typical system, namely acoustic (AM), pronunciation (PM) and language (LM) models into a single neural network. In this work, we look at one such sequence-to-…
Mask based statistical beamforming, where signal statistics for the target and the interference gained from masking are used for beamforming, has shown great effectiveness in the two recent CHiME challenges. This idea has sparked interest in the research community and resulted in numerous proposed a…
This article introduces and evaluates Sampled Connectionist Temporal Classification (CTC) which connects the CTC criterion to the Cross Entropy (CE) objective through sampling. Instead of computing the logarithm of the sum of the alignment path likelihoods, at each training step the sampled CTC only…
In this paper, we present an algorithm called Reliable Mask Selection-Phase Difference Channel Weighting (RMS-PDCW) which selects the target source masked by a noise source using the Angle of Arrival (AoA) information calculated using the phase difference information. The RMS-PDCW algorithm selects…
In this paper, we present an algorithm which introduces phase-perturbation to the training database when training phase-sensitive deep neural-network models. Traditional features such as log-mel or cepstral features do not have have any phase-relevant information. However features such as raw-wavefo…
Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic speech recognition (ASR) system into a single neural network. In previous work, we have shown that such architectures ar…
Multichannel ASR systems commonly separate speech enhancement, including localization, beamforming and postfiltering, from acoustic modeling. Recently, we explored doing multichannel enhancement jointly with acoustic modeling, where beamforming and frequency decomposition was folded into one layer o…