538 research outputs found
Towards Understanding Egyptian Arabic Dialogues
Labelling of user's utterances to understanding his attends which called
Dialogue Act (DA) classification, it is considered the key player for dialogue
language understanding layer in automatic dialogue systems. In this paper, we
proposed a novel approach to user's utterances labeling for Egyptian
spontaneous dialogues and Instant Messages using Machine Learning (ML) approach
without relying on any special lexicons, cues, or rules. Due to the lack of
Egyptian dialect dialogue corpus, the system evaluated by multi-genre corpus
includes 4725 utterances for three domains, which are collected and annotated
manually from Egyptian call-centers. The system achieves F1 scores of 70. 36%
overall domains.Comment: arXiv admin note: substantial text overlap with arXiv:1505.0308
Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition
Crafting an effective Automatic Speech Recognition (ASR) solution for
dialects demands innovative approaches that not only address the data scarcity
issue but also navigate the intricacies of linguistic diversity. In this paper,
we address the aforementioned ASR challenge, focusing on the Tunisian dialect.
First, textual and audio data is collected and in some cases annotated. Second,
we explore self-supervision, semi-supervision and few-shot code-switching
approaches to push the state-of-the-art on different Tunisian test sets;
covering different acoustic, linguistic and prosodic conditions. Finally, and
given the absence of conventional spelling, we produce a human evaluation of
our transcripts to avoid the noise coming from spelling inadequacies in our
testing references. Our models, allowing to transcribe audio samples in a
linguistic mix involving Tunisian Arabic, English and French, and all the data
used during training and testing are released for public use and further
improvements.Comment: 6 pages, submitted to ICASSP 202
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