In this article we present a study of a multi-accent and accentindependent non-native speech recognition. We propose several approaches based on phonetic confusion and acoustic adaptation. The goal of this article is to investigate the feasibility of multi-accent non-native speech recognition without detecting the origin of the speaker. Tests on the HIWIRE corpus show that multi-accent pronunci...

To support summarization of automatically transcribed meetings, we introduce a classifier to recognize agreement or disagreement utterances, utilizing both word-based and prosodic cues. We show that hand-labeling efforts can be minimized by using unsupervised training on a large unlabeled data set combined with supervised training on a small amount of data. For ASR transcripts with over 45% WER...

Non-native speakers often face difficulty in pronouncing like the native speakers. This paper proposes to model pronunciation variation in non-native speaker’s speech using only acoustics models, without the need for the corpus. Variation in term of context and language will be modeled. The combination of both modeling resulted in the reduction of absolute WER as much as 16% and 6% for native V...

In this paper, we propose an automatic scoring approach for assessing the language deficit in a sentence repetition task used to evaluate children with language disorders. From ASR-transcribed sentences, we extract sentence similarity measures, including WER and Levenshtein distance, and use them as the input features in a regression model to predict the reference scores manually rated by exper...

Previous work on self-training of acoustic models using unlabeled data reported significant reductions in WER assuming a large phonetic dictionary was available. We now assume only those words from ten hours of speech are initially available. Subsequently, we are then given a large vocabulary and then quantify the value of repeating selftraining with this larger dictionary. This experiment is u...

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Los 14 trabajos seleccionados son publicados en el presente proceedings por la organización del WER y los estos mismos documentos serán luego puestos a disposición en el repositorio

A new adaptation method called inter-class MLLR has recently been introduced. Inter-class MLLR utilizes relationships among different transformation functions to achieve more reliable estimates of MLLR parameters across multiple classes, and it produces lower word error rates (WER) than conventional MLLR in circumstances where very little speaker-specific adaptation data are available. This pap...