Search Results for author:
Found 9 papers, 1 papers with code
WHISMA: A Speech-LLM to Perform Zero-shot Spoken Language Understanding
Speech large language models (speech-LLMs) integrate speech and text-based foundation models to provide a unified framework for handling a wide range of downstream tasks.
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis
TTS systems are trained with a small amount of accented speech training data and their pseudo-labels rather than manual transcriptions, and hence unsupervised.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Multiple-hypothesis RNN-T Loss for Unsupervised Fine-tuning and Self-training of Neural Transducer
The multiple-hypothesis approach yields a relative reduction of 3. 3% WER on the CHiME-4's single-channel real noisy evaluation set when compared with the single-hypothesis approach.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Robust multi-sensor Generalized Labeled Multi-Bernoulli filter
This paper proposes an efficient and robust algorithm to estimate target trajectories with unknown target detection profiles and clutter rates using measurements from multiple sensors.
Multiple-hypothesis CTC-based semi-supervised adaptation of end-to-end speech recognition
In this method, multiple automatic speech recognition (ASR) 1-best hypotheses are integrated in the computation of the connectionist temporal classification (CTC) loss function.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Train your classifier first: Cascade Neural Networks Training from upper layers to lower layers
Although the lower layers of a deep neural network learn features which are transferable across datasets, these layers are not transferable within the same dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multi-object Tracking with an Adaptive Generalized Labeled Multi-Bernoulli Filter
The challenges in multi-object tracking mainly stem from the random variations in the cardinality and states of objects during the tracking process.
Top-down training for neural networks
Interpreting the top layers as a classifier and the lower layers a feature extractor, one can hypothesize that unwanted network convergence may occur when the classifier has overfit with respect to the feature extractor.
End-to-End Speech Recognition with High-Frame-Rate Features Extraction
On WSJ corpus, the relative reduction of word error rate (WER) yielded by high-frame-rate features extraction independently and in combination with speed perturbation are up to 21. 3% and 24. 1%, respectively.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
