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Improving Speech Recognition Using a Data-Driven Approach

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Towards using slide information to enhance speech transcription of meetings

Hervé Bourlard, Artem Peregoudov, Alessandro Vinciarelli

In this paper we investigate the possibility of improving the speech recognition performance of meeting recordings by using slides captured during the recording process. The key hypothesis exploited in this work is that both slides and speech carry correla ...

IDIAP2006

Improving Speech Recognition Using a Data-Driven Approach

Hervé Bourlard, Guillermo Aradilla

In this paper, we investigate the possibility of enhancing state-of-the-art HMM-based speech recognition systems using data-driven techniques, where whole set of training utterances is used as reference models and recognition is then performed through the ...

IDIAP2005

Using Pitch as Prior Knowledge in Template-Based Speech Recognition

Hervé Bourlard, Guillermo Aradilla

In a previous paper on speech recognition, we showed that templates can better capture the dynamics of speech signal compared to parametric models such as hidden Markov models. The key point in template matching approaches is finding the most similar templ ...

IDIAP2005

Efficient integration of automated speech recognition in the framework of dialogue-based vocal systems

In this work, we propose different strategies for efficiently integrating an automated speech recognition module in the framework of a dialogue-based vocal system. The aim is the study of different ways leading to the improvement of the quality and robustn ...

EPFL2005

Multimedia event modelling and recognition

Mark Barnard

The recognition of events in multimedia data is a challenging area of research. The growth in the amount of multimedia data being produced and stored increases the need for systems capable of automatically analysing this data. This analysis can aid in effi ...

EPFL2005

Noisy Text Categorization

Alessandro Vinciarelli

This work presents categorization experiments performed over noisy texts. By noisy it is meant any text obtained through an extraction process (affected by errors) from media other than digital texts (e.g. transcriptions of speech recordings extracted with ...

2005

Application of Information Retrieval Techniques to Single Writer Documents

Alessandro Vinciarelli

This work shows Information Retrieval experiments performed over handwritten documents produced by a single writer. The same retrieval task has been performed over both manual (no errors) and automatic (Word Error Rate around 45%) transcriptions of 200 han ...

2005

Noisy Text Clustering

David Grangier, Alessandro Vinciarelli

This work presents document clustering experiments performed over noisy texts (i.e. text that have been extracted through an automatic process like speech or character recognition). The effect of recognition errors on different clustering techniques is mea ...

IDIAP2004

Spectro-Temporal Activity Pattern (STAP) Features for Noise Robust ASR

Hervé Bourlard, Hemant Misra, Shajith Ikbal

In this paper, we introduce a new noise robust representation of speech signal obtained by locating points of potential importance in the spectrogram, and parameterizing the activity of time-frequency pattern around those points. These features are referre ...

2004

Spectro-Temporal Activity Pattern (STAP) Features for Noise Robust ASR

Hervé Bourlard, Hemant Misra, Shajith Ikbal

IDIAP2004