Search Results for author:
Found 13 papers, 5 papers with code
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models
We conducted extensive experiments with a 2-billion parameter USM on a large-scale voice search dataset to evaluate our proposed method.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
2-bit Conformer quantization for automatic speech recognition
With the large-scale training data, we obtain a 2-bit Conformer model with over 40% model size reduction against the 4-bit version at the cost of 17% relative word error rate degradation
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
RAND: Robustness Aware Norm Decay For Quantized Seq2seq Models
With the rapid increase in the size of neural networks, model compression has become an important area of research.
Sharing Low Rank Conformer Weights for Tiny Always-On Ambient Speech Recognition Models
Such a model allows us to achieve always-on ambient speech recognition on edge devices with low-memory neural processors.
A Unified Cascaded Encoder ASR Model for Dynamic Model Sizes
In this paper, we propose a dynamic cascaded encoder Automatic Speech Recognition (ASR) model, which unifies models for different deployment scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Personal VAD 2.0: Optimizing Personal Voice Activity Detection for On-Device Speech Recognition
Personalization of on-device speech recognition (ASR) has seen explosive growth in recent years, largely due to the increasing popularity of personal assistant features on mobile devices and smart home speakers.
4-bit Conformer with Native Quantization Aware Training for Speech Recognition
Reducing the latency and model size has always been a significant research problem for live Automatic Speech Recognition (ASR) application scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Towards Lifelong Learning of Multilingual Text-To-Speech Synthesis
This work presents a lifelong learning approach to train a multilingual Text-To-Speech (TTS) system, where each language was seen as an individual task and was learned sequentially and continually.
Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable
In this paper, we investigate the tantalizing possibility of using lottery ticket hypothesis to discover lightweight speech recognition models, that are (1) robust to various noise existing in speech; (2) transferable to fit the open-world personalization; and 3) compatible with structured sparsity.
Textual Echo Cancellation
In this paper, we propose Textual Echo Cancellation (TEC) - a framework for cancelling the text-to-speech (TTS) playback echo from overlapping speech recordings.
AutoSpeech: Neural Architecture Search for Speaker Recognition
Speaker recognition systems based on Convolutional Neural Networks (CNNs) are often built with off-the-shelf backbones such as VGG-Net or ResNet.
Ranked #8 on
Speaker Identification
on VoxCeleb1
(using extra training data)
Personal VAD: Speaker-Conditioned Voice Activity Detection
In this paper, we propose "personal VAD", a system to detect the voice activity of a target speaker at the frame level.
ABD-Net: Attentive but Diverse Person Re-Identification
Attention mechanism has been shown to be effective for person re-identification (Re-ID).
Ranked #15 on
Person Re-Identification
on Market-1501-C
