Paper ID | SPE-14.2 |
Paper Title |
SPARSIFICATION VIA COMPRESSED SENSING FOR AUTOMATIC SPEECH RECOGNITION |
Authors |
Kai Zhen, Indiana University Bloomington, United States; Hieu Nguyen, Feng-Ju Chang, Athanasios Mouchtaris, Ariya Rastrow, Amazon, United States |
Session | SPE-14: Speech Recognition 6: New Algorithms for Sparsity/Efficiency |
Location | Gather.Town |
Session Time: | Wednesday, 09 June, 13:00 - 13:45 |
Presentation Time: | Wednesday, 09 June, 13:00 - 13:45 |
Presentation |
Poster
|
Topic |
Speech Processing: [SPE-GASR] General Topics in Speech Recognition |
IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
Virtual Presentation |
Click here to watch in the Virtual Conference |
Abstract |
In order to achieve high accuracy for machine learning (ML) applications, it is essential to employ models with a large number of parameters. Certain applications, such as Automatic Speech Recognition (ASR), however, require real-time interactions with users, hence compelling the model to have as low latency as possible. Deploying large scale ML applications thus necessitates model quantization and compression, especially when running ML models on resource constrained devices. For example, by forcing some of the model weight values into zero, it is possible to apply zero-weight compression, which reduces both the model size and model reading time from the memory. In the literature, such methods are referred to as sparse pruning. The fundamental questions are when and which weights should be forced to zero, i.e. be pruned. In this work, we propose a compressed sensing based pruning (CSP) approach to effectively address those questions. By reformulating sparse pruning as a sparsity inducing and compression-error reduction dual problem, we introduce the classic compressed sensing process into the ML model training process. Using ASR task as an example, we show that CSP consistently outperforms existing approaches in the literature. |