12 accepted papers
This paper presents a novel Dialectal Sound and Vowelization Recovery framework, designed to recognize borrowed and dialectal sounds within phonologically diverse and dialect-rich languages, that extends beyond its standard orthographic sound sets. The proposed framework utilized quantized sequence…
The phonological discrepancies between a speaker’s native (L1) and the non-native language (L2) serves as a major factor for mispronunciation. This paper introduces a novel multilingual Mispronunciation Detection and Diagnosis (MDD) architecture, L1-MultiMDD, enriched with L1-aware speech representa…
Designing effective automatic speech recognition (ASR) systems for Code-Switching (CS) often depends on the availability of the transcribed CS resources. To address data scarcity, this paper introduces Speech Collage, a method that synthesizes CS data from monolingual corpora by splicing audio segme…
Pronunciation assessment and its application in computer-aided pronunciation training (CAPT) have seen impressive progress in recent years. With the rapid growth in language processing and deep learning over the past few years, there is a need for an updated review. In this paper, we review methods…
The success of the multilingual automatic speech recognition systems empowered many voice-driven applications. However, measuring the performance of such systems remains a major challenge, due to its dependency on manually transcribed speech data in both mono- and multilingual scenarios. In this pap…
Epilepsy is a neurological disorder characterized by seizures that can disrupt a patient’s quality of life. EEG has been used to detect underlying neural activity for diagnosis and treatment. However, standard methods of seizure detection are time-consuming and require manual detection by a trained…
We introduce the largest transcribed Arabic speech corpus, QASR, collected from the broadcast domain. This multi-dialect speech dataset contains 2,000 hours of speech sampled at 16kHz crawled from Aljazeera news channel. The dataset is released with lightly supervised transcriptions, aligned with th…
In this paper, we describe a method to collect dialectal speech from YouTube videos to create a large-scale Dialect Identification (DID) dataset. Using this method, we collected dialectal Arabic from known YouTube channels from 17 Arabic speaking countries in the Middle East and Northern Africa. Aft…
We present the Factorial Deep Markov Model (FDMM) for representation learning of speech. The FDMM learns disentangled, interpretable and lower dimensional latent representations from speech without supervision. We use a static and dynamic latent variable to exploit the fact that information in a spe…
End-to-end deep learning language or dialect identification systems operate on the spectrogram or other acoustic feature and directly generate identification scores for each class. An important issue for end-to-end systems is to have some knowledge of the application domain, because the system can b…
Emotion recognition from speech signal based on deep learning is an active research area. Convolutional neural networks (CNNs) may be the dominant method in this area. In this paper, we implement two neural architectures to address this problem. The first architecture is an attention-based CNN-LSTM-…
In this paper, we investigate different approaches for Dialect Identification (DID) in Arabic broadcast speech. Dialects differ in their inventory of phonological segments. This paper proposes a new phonotactic based feature representation approach which enables discrimination among different occurr…