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Found 12 papers, 0 papers with code
Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition
Recent advances in machine learning have demonstrated that multi-modal pre-training can improve automatic speech recognition (ASR) performance compared to randomly initialized models, even when models are fine-tuned on uni-modal tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Turn-taking and Backchannel Prediction with Acoustic and Large Language Model Fusion
We propose an approach for continuous prediction of turn-taking and backchanneling locations in spoken dialogue by fusing a neural acoustic model with a large language model (LLM).
Two-pass Endpoint Detection for Speech Recognition
Endpoint (EP) detection is a key component of far-field speech recognition systems that assist the user through voice commands.
Cross-utterance ASR Rescoring with Graph-based Label Propagation
We propose a novel approach for ASR N-best hypothesis rescoring with graph-based label propagation by leveraging cross-utterance acoustic similarity.
Guided contrastive self-supervised pre-training for automatic speech recognition
Contrastive Predictive Coding (CPC) is a representation learning method that maximizes the mutual information between intermediate latent representations and the output of a given model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
ASR-Aware End-to-end Neural Diarization
We present a Conformer-based end-to-end neural diarization (EEND) model that uses both acoustic input and features derived from an automatic speech recognition (ASR) model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Audiovisual Highlight Detection in Videos
To better study the task of highlight detection, we run a pilot experiment with highlights annotations for a small subset of video clips and fine-tune our best model on it.
Self-Supervised learning with cross-modal transformers for emotion recognition
Self-supervised learning has shown improvements on tasks with limited labeled datasets in domains like speech and natural language.
Multi-modal embeddings using multi-task learning for emotion recognition
General embeddings like word2vec, GloVe and ELMo have shown a lot of success in natural language tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Multimodal and Multiresolution Speech Recognition with Transformers
We particularly focus on the scene context provided by the visual information, to ground the ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multiresolution and Multimodal Speech Recognition with Transformers
We particularly focus on the scene context provided by the visual information, to ground the ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Fully Learnable Front-End for Multi-Channel Acoustic Modeling using Semi-Supervised Learning
Using a large offline teacher model trained on beamformed audio, we trained a simpler multi-channel student acoustic model used in the speech recognition system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
