Speech recognition errors affect the performance of multimodal systems especially in noisy conditions. For that reason the use of speech recognition confidence score and time stamps is suggested. This paper deals with the impact of a signal enhancement method on these speech recognition’s parameters, in the presence of noise. By recognising noisy signals not only the word error rate (WER) incre...

The aim of the study was to explore if a long reverberation has the same effect on recall of spoken words as background noise was shown to have in a previous study. A further aim was to study the role of working memory capacity for performance in these conditions. Thirty-two subjects performed a word recall and a sentence recognition test. They repeated each word to ensure correct identificatio...

and Recognition Probabilities G unther G orz, Gerhard Hanrieder Bavarian Research Center for Knowledge Based Systems (FORWISS) Am Weichselgarten 7, 91058 Erlangen, Germany ABSTRACT In this paper we describe the linguistic processing component of a spoken dialogue system. The task of this word graph parser is to nd the most plausible sequence of word hypotheses in the input graph. If no global...

Most state-of-the-art approaches for namedentity recognition (NER) use semi supervised information in the form of word clusters and lexicons. Recently neural network-based language models have been explored, as they as a byproduct generate highly informative vector representations for words, known as word embeddings. In this paper we present two contributions: a new form of learning word embedd...

In this paper we address the issue of recognizing Farsi handwritten words. Two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. These are directional and intensity gradient features. The feature vector extracted from each stripe is then coded using the Self Organizing Map (SOM). In this method each word is modeled using the discrete Hidde...

So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka. nested), and discontinuous NER, which have mostly studied individually. Recently, a growing interest built for unified tackling the above jobs concurrently one single model. Current best-performing methods mainly include span-based sequence-to-sequence models, where unfortunately fo...

Named Entity Recognition is a crucial component in bio-medical text mining.In this paper a method for disease Named Entity Recognition is proposed which utilizes sentence and token level features based on Conditional Random Field’s using NCBI disease corpus. The feature set used for the experiment includes orthographic,contextual,affixes,ngrams,part of speech tags and word normalization.Using t...

This paper proposes a new method of automatically summarizing speech by extracting a limited number of relatively important words from its automatic transcription according to a target compression ratio for the number of characters. To determine a word set to be extracted, we de ne a summarization score consisting of a topic score (signi cance measure) of words and a linguistic score (likelihoo...