Deep learning methods for Automatic Speech Recognition (ASR) often rely on large-scale
training datasets, which are typically unavailable in low-resource environments (LREs). This …

Accent identification and classification pose a major challenge for speech recognition
systems, as various pronunciations of the same words by speakers of different races are …

Speaker diarization, the process of segmenting audio into speaker-specific regions, plays a
critical role in various speech technologies by determining" who spoke when" in a …

Nous abordons, dans cette thèse, une étude sur les représentations continues de mots (en
anglais word embeddings) appliquées à la détection automatique des erreurs dans les …

For the past few years, artificial neural networks (ANNs) have been one of the most common
solutions relied upon while developing automated speech recognition (ASR) acoustic …

This paper explores state-of-the-art techniques for creating language models in low-
resource setting. It is known that building a good statistical language model requires a large …

ABSTRACT A pronunciation dictionary (PD) is one of the components in an Automatic
Speech Recognition (ASR) system, a system that is used to convert speech to text. The …

Cette thèse s' inscrit dans le cadre d'une étude sur le potentiel de la transcription
automatique pour l'instrumentation de situations pédagogiques. Notre contribution porte sur …

Research interest in recognizing and synthesizing accents dates back to several decades
ago, but just until the mid-twentieth century that the automatic speech recognition (ASR) …

Computational morphology covers the automatic analysis (recognition of the internal
structure) and generation (formation of a word) of words. As such, it is an ineluctable step in …