Search Results for author:
Found 21 papers, 4 papers with code
Automatic Dialect Detection in Arabic Broadcast Speech
We used these features in a binary classifier to discriminate between Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100%.
Multi-view Dimensionality Reduction for Dialect Identification of Arabic Broadcast Speech
In this work, we present a new Vector Space Model (VSM) of speech utterances for the task of spoken dialect identification.
The SUMMA Platform Prototype
We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
QCRI Live Speech Translation System
This paper presents QCRI{'}s Arabic-to-English live speech translation system.
DARTS: Dialectal Arabic Transcription System
We analyze the following; transfer learning from high resource broadcast domain to low-resource dialectal domain and semi-supervised learning where we use in-domain unlabeled audio data collected from YouTube.
Robust Training of Vector Quantized Bottleneck Models
We show that the codebook learning can suffer from poor initialization and non-stationarity of clustered encoder outputs.
A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning
Probabilistic Latent Variable Models (LVMs) provide an alternative to self-supervised learning approaches for linguistic representation learning from speech.
CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning
The audio encoder is trained to perform a speech-translation retrieval task in a contrastive learning framework.
Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training
The performance of automatic speech recognition (ASR) systems typically degrades significantly when the training and test data domains are mismatched.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition
We investigate the existence of sparse subnetworks in pre-trained speech SSL models that achieve even better low-resource ASR results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0
We propose a simple and effective cross-lingual transfer learning method to adapt monolingual wav2vec-2. 0 models for Automatic Speech Recognition (ASR) in resource-scarce languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
CMKD: CNN/Transformer-Based Cross-Model Knowledge Distillation for Audio Classification
Audio classification is an active research area with a wide range of applications.
SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation
We combine state-of-the-art multilingual acoustic frame-level speech representation learning model XLS-R with the Language Agnostic BERT Sentence Embedding (LaBSE) model to create an utterance-level multimodal multilingual speech encoder SAMU-XLSR.
On Unsupervised Uncertainty-Driven Speech Pseudo-Label Filtering and Model Calibration
Dropout-based Uncertainty-driven Self-Training (DUST) proceeds by first training a teacher model on source domain labeled data.
Comparison of Multilingual Self-Supervised and Weakly-Supervised Speech Pre-Training for Adaptation to Unseen Languages
Recent models such as XLS-R and Whisper have made multilingual speech technologies more accessible by pre-training on audio from around 100 spoken languages each.
Improved Cross-Lingual Transfer Learning For Automatic Speech Translation
Having a single model that supports multiple translation tasks is desirable.
Direct Text to Speech Translation System using Acoustic Units
This framework employs text in different source languages as input to generate speech in the target language without the need for text transcriptions in this language.
Generation or Replication: Auscultating Audio Latent Diffusion Models
The introduction of audio latent diffusion models possessing the ability to generate realistic sound clips on demand from a text description has the potential to revolutionize how we work with audio.
Scenario-Aware Audio-Visual TF-GridNet for Target Speech Extraction
Target speech extraction aims to extract, based on a given conditioning cue, a target speech signal that is corrupted by interfering sources, such as noise or competing speakers.
NeuroHeed+: Improving Neuro-steered Speaker Extraction with Joint Auditory Attention Detection
Neuro-steered speaker extraction aims to extract the listener's brain-attended speech signal from a multi-talker speech signal, in which the attention is derived from the cortical activity.
NIIRF: Neural IIR Filter Field for HRTF Upsampling and Personalization
Existing NF-based methods focused on estimating the magnitude of the HRTF from a given sound source direction, and the magnitude is converted to a finite impulse response (FIR) filter.
