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Found 31 papers, 14 papers with code
Clinical Annotations for Automatic Stuttering Severity Assessment
Stuttering is a complex disorder that requires specialized expertise for effective assessment and treatment.
ArVoice: A Multi-Speaker Dataset for Arabic Speech Synthesis
We introduce ArVoice, a multi-speaker Modern Standard Arabic (MSA) speech corpus with diacritized transcriptions, intended for multi-speaker speech synthesis, and can be useful for other tasks such as speech-based diacritic restoration, voice conversion, and deepfake detection.
Voice of a Continent: Mapping Africa's Speech Technology Frontier
Africa's rich linguistic diversity remains significantly underrepresented in speech technologies, creating barriers to digital inclusion.
SPIRIT: Patching Speech Language Models against Jailbreak Attacks
Speech Language Models (SLMs) enable natural interactions via spoken instructions, which more effectively capture user intent by detecting nuances in speech.
JEEM: Vision-Language Understanding in Four Arabic Dialects
We introduce JEEM, a benchmark designed to evaluate Vision-Language Models (VLMs) on visual understanding across four Arabic-speaking countries: Jordan, The Emirates, Egypt, and Morocco.
Infant Cry Detection Using Causal Temporal Representation
This paper addresses a major challenge in acoustic event detection, in particular infant cry detection in the presence of other sounds and background noises: the lack of precise annotated data.
SparQLe: Speech Queries to Text Translation Through LLMs
With the growing influence of Large Language Models (LLMs), there is increasing interest in integrating speech representations with them to enable more seamless multi-modal processing and speech understanding.
Dialectal Coverage And Generalization in Arabic Speech Recognition
Developing robust automatic speech recognition (ASR) systems for Arabic requires effective strategies to manage its diversity.
STTATTS: Unified Speech-To-Text And Text-To-Speech Model
Speech recognition and speech synthesis models are typically trained separately, each with its own set of learning objectives, training data, and model parameters, resulting in two distinct large networks.
RelUNet: Relative Channel Fusion U-Net for Multichannel Speech Enhancement
Neural multi-channel speech enhancement models, in particular those based on the U-Net architecture, demonstrate promising performance and generalization potential.
PALM: Few-Shot Prompt Learning for Audio Language Models
We demonstrate the effectiveness of our approach on 11 audio recognition datasets, encompassing a variety of speech-processing tasks, and compare the results with three baselines in a few-shot learning setup.
Mixat: A Data Set of Bilingual Emirati-English Speech
This paper introduces Mixat: a dataset of Emirati speech code-mixed with English.
Spoken Word2Vec: Learning Skipgram Embeddings from Speech
Text word embeddings that encode distributional semantics work by modeling contextual similarities of frequently occurring words.
Automatic Restoration of Diacritics for Speech Data Sets
Automatic text-based diacritic restoration models generally have high diacritic error rates when applied to speech transcripts as a result of domain and style shifts in spoken language.
ArTST: Arabic Text and Speech Transformer
We present ArTST, a pre-trained Arabic text and speech transformer for supporting open-source speech technologies for the Arabic language.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Yet Another Model for Arabic Dialect Identification
We explore two architectural variations: ResNet and ECAPA-TDNN, coupled with two types of acoustic features: MFCCs and features exratected from the pre-trained self-supervised model UniSpeech-SAT Large, as well as a fusion of all four variants.
Adapting the adapters for code-switching in multilingual ASR
Recently, large pre-trained multilingual speech models have shown potential in scaling Automatic Speech Recognition (ASR) to many low-resource languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Handling Realistic Label Noise in BERT Text Classification
Labels noise refers to errors in training labels caused by cheap data annotation methods, such as web scraping or crowd-sourcing, which can be detrimental to the performance of supervised classifiers.
ClArTTS: An Open-Source Classical Arabic Text-to-Speech Corpus
In a move towards filling this gap in resources, we present a speech corpus for Classical Arabic Text-to-Speech (ClArTTS) to support the development of end-to-end TTS systems for Arabic.
Diacritic Recognition Performance in Arabic ASR
We present an analysis of diacritic recognition performance in Arabic Automatic Speech Recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Supervised Acoustic Embeddings And Their Transferability Across Languages
In speech recognition, it is essential to model the phonetic content of the input signal while discarding irrelevant factors such as speaker variations and noise, which is challenging in low-resource settings.
Unsupervised Automatic Speech Recognition: A Review
Automatic Speech Recognition (ASR) systems can be trained to achieve remarkable performance given large amounts of manually transcribed speech, but large labeled data sets can be difficult or expensive to acquire for all languages of interest.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Homograph Disambiguation Through Selective Diacritic Restoration
Diacritic restoration could theoretically help disambiguate these words, but in practice, the increase in overall sparsity leads to performance degradation in NLP applications.
Efficient Sentence Embedding using Discrete Cosine Transform
Vector averaging remains one of the most popular sentence embedding methods in spite of its obvious disregard for syntactic structure.
Scalable Cross-Lingual Transfer of Neural Sentence Embeddings
We develop and investigate several cross-lingual alignment approaches for neural sentence embedding models, such as the supervised inference classifier, InferSent, and sequential encoder-decoder models.
Evaluation of Unsupervised Compositional Representations
We evaluated various compositional models, from bag-of-words representations to compositional RNN-based models, on several extrinsic supervised and unsupervised evaluation benchmarks.
Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings
We show empirically that the performance of bilingual correspondents learned using our proposed unsupervised method is comparable to that of using supervised bilingual correspondents from a seed dictionary.
