10 accepted papers
We introduce DAS (Domain Adaptation with Synthetic data), a novel domain adaptation framework for pre-trained ASR model, designed to efficiently adapt to various language-defined domains without requiring any real data. In particular, DAS first prompts large language models (LLMs) to generate domain…
Modern smart glasses leverage machine learning to offer real-time transcriptions, considerably enriching human communication experiences. However, such systems frequently encounter challenges related to environmental noises, leading to decreased speech recognition. To improve voice quality, this wor…
Automatic Speech Recognition (ASR) models need to be optimized for specific hardware before they can be deployed on devices. This can be done by tuning the model’s hyperparameters or exploring variations in its architecture. Re-training and re-validating models after making these changes can be a re…
A keyword spotting (KWS) engine continuously running on the device is exposed to various speech signals that are usually unseen beforehand. It is a challenging problem to build a small-footprint and high-performing KWS model with robustness under different acoustic environments. In this paper, we ex…
End-to-End deep learning has shown promising results for speech enhancement tasks, such as noise suppression, dereverberation, and speech separation. However, most state-of-the-art methods for echo cancellation are either classical DSP-based or hybrid DSP-ML algorithms. Components such as the delay…
Speech codec compresses the input signal into compact bit stream, which is then decoded at the receiver to generate the best possible perceptual quality. This compression makes storing and transmitting speech efficient. In this work, we propose a neural extension to low bit rate speech codec (e.g.,…
In this paper, we explore the use of adversarial examples for improving a neural network based keyword spotting (KWS) system. Specially, in our system, an effective and small-footprint attention-based neural network model is used. Adversarial example is defined as a misclassified example by a model,…
Visual object recognition under situations in which the direct line-of-sight is blocked, such as when it is occluded around the corner, is of practical importance in a wide range of applications. With coherent illumination, the light scattered from diffusive walls forms speckle patterns that contain…
Long Short Term Memory Connectionist Temporal Classification (LSTM-CTC) based end-to-end models are widely used in speech recognition due to its simplicity in training and efficiency in decoding. In conventional LSTM-CTC based models, a bottleneck projection matrix maps the hidden feature vectors ob…
Recurrent Neural Networks (RNNs) have dominated language modeling because of their superior performance over traditional N-gram based models. In many applications, a large Recurrent Neural Network language model (RNNLM) or an ensemble of several RNNLMs is used. These models have large memory footpri…