Author: Dimitra Vergyri

In this paper we investigate different procedures that enable us to use training data by automatically inserting the missing diacritics into the transcription.

We augment the Mel cepstral (MFCC) feature representation with voicing features from an independent front end.

In this paper we describe the use of acoustic data from Modern Standard Arabic (MSA) to improve the recognition of Egyptian Conversational Arabic (ECA).

This paper describes a prototype system for near-real-time spontaneous, bidirectional translation between spoken English and Pashto.

We describe the development and conceptual evolution of handheld spoken phrase translation systems, beginning with an initial undirectional system for translation of English phrases, and later extending to a limited bidirectional phrase translation system.

This paper reports on our project at the 2002 Johns Hopkins Summer Workshop, which focused on the recognition of dialectal Arabic.

We investigate three models, each exploiting a different level of prosodic information, in rescoring N-best hypotheses according to how well recognized words correspond to prosodic features of the utterance.

We describe SRI’s recognition system as used in the 2001 DARPA Speech in Noisy Environments (SPINE) evaluation. The SPINE task involves recognition of speech in simulated military environments.

In this work we demonstrate an improvement in the state-of-the-art large vocabulary continuous speech recognition (LVCSR) performance, under clean and noisy conditions, by the use of visual information, in addition to the traditional audio one.

The procedure we propose in this paper aims to em cleanse/ such quick transcriptions so that they align better with the acoustic evidence and thus provide for better acoustic models for automatic speech recognition (ASR).