Towards Robust Understanding of Speech

Towards robust conversational speech recognition and understanding

An Information-Theoretic Discussion of Convolutional Bottleneck Features for Robust Speech Recognition

Convolutional Neural Networks (CNNs) have been shown their performance in speech recognition systems for extracting features, and also acoustic modeling. In addition, CNNs have been used for robust speech recognition and competitive results have been reported. Convolutive Bottleneck Network (CBN) is a kind of CNNs which has a bottleneck layer among its fully connected layers. The bottleneck fea...

Improving the performance of MFCC for Persian robust speech recognition

The Mel Frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. In this paper to achieve a satisfactorily performance in Automatic Speech Recognition (ASR) applications we introduce a noise robust new set of MFCC vector estimated through following steps. First, spectral mean normalization is a pre-processing which applies to t...

Robust dependency parsing for spoken language understanding of spontaneous speech

We describe in this paper a syntactic parser for spontaneous speech geared towards the identification of verbal subcategorization frames. The parser proceeds in two stages. The first stage is based on generic syntactic resources for French. The second stage is a reranker which is specially trained for a given application. The parser is evaluated on the MEDIA corpus.

Robust methods in automatic speech recognition and understanding

This paper overviews robust architecture and modeling techniques for automatic speech recognition and understanding. The topics include robust acoustic and language modeling for spontaneous speech recognition, unsupervised adaptation of acoustic and language models, robust architecture for spoken dialogue systems, multi-modal speech recognition, and speech understanding. This paper also discuss...