Paper Reading AI Learner

Towards Green ASR: Lossless 4-bit Quantization of a Hybrid TDNN System on the 300-hr Switchboard Corpus

2022-06-23 12:02:33
Junhao Xu, Shoukang Hu, Xunying Liu, Helen Meng

Abstract

State of the art time automatic speech recognition (ASR) systems are becoming increasingly complex and expensive for practical applications. This paper presents the development of a high performance and low-footprint 4-bit quantized LF-MMI trained factored time delay neural networks (TDNNs) based ASR system on the 300-hr Switchboard corpus. A key feature of the overall system design is to account for the fine-grained, varying performance sensitivity at different model components to quantization errors. To this end, a set of neural architectural compression and mixed precision quantization approaches were used to facilitate hidden layer level auto-configuration of optimal factored TDNN weight matrix subspace dimensionality and quantization bit-widths. The proposed techniques were also used to produce 2-bit mixed precision quantized Transformer language models. Experiments conducted on the Switchboard data suggest that the proposed neural architectural compression and mixed precision quantization techniques consistently outperform the uniform precision quantised baseline systems of comparable bit-widths in terms of word error rate (WER). An overall "lossless" compression ratio of 13.6 was obtained over the baseline full precision system including both the TDNN and Transformer components while incurring no statistically significant WER increase.

Abstract (translated)

URL

https://arxiv.org/abs/2206.11643

PDF

https://arxiv.org/pdf/2206.11643.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot