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Automatic Speech Recognition Performance Improvement for Mandarin Based on Optimizing Gain Control Strategy.

Desheng Wang¹, Yangjie Wei¹, Ke Zhang¹

¹Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China.

Sensors (Basel, Switzerland)

|April 23, 2022

Summary

This summary is machine-generated.

This study introduces a new gain control strategy for automatic speech recognition (ASR) to reduce word error rate (WER). The proposed maximized original signal transmission (MOST) method significantly lowers WER in ASR systems.

Keywords:

automatic speech recognition (ASR)gain control human–computer interaction maximized original signal transmission (MOST)noise figure word error rate (WER)

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Area of Science:

Speech Processing
Human-Computer Interaction
Signal Processing

Background:

Automatic speech recognition (ASR) relies on gain control, but suboptimal strategies increase word error rate (WER).
A theoretical gap exists in understanding the precise relationship between gain control and ASR performance metrics like WER.
Current ASR systems often employ unconstrained gain control, failing to achieve optimal performance and minimize errors.

Purpose of the Study:

To propose and validate a novel gain control strategy, maximized original signal transmission (MOST), to mitigate the negative impact of gain control on ASR systems.
To establish a quantitative relationship between gain control strategies and ASR performance using the noise figure index.
To theoretically deduce an optimal MOST gain control strategy that minimizes performance degradation.

Main Methods:

Modeling gain control strategies to establish a quantitative link with ASR performance via the noise figure index.
Analyzing the derived quantitative relationship to theoretically determine the optimal MOST gain control parameters.
Conducting comprehensive comparative experiments on a Mandarin speech dataset to evaluate the proposed strategy.

Main Results:

A quantitative relationship between gain control and ASR performance was established using the noise figure index.
The MOST gain control strategy was theoretically deduced to minimize performance degradation.
Experimental results demonstrated a significant reduction in WER for the ASR system using the MOST strategy.

Conclusions:

The proposed MOST gain control strategy effectively minimizes the adverse effects of gain control on ASR systems.
The MOST strategy achieved a 10% mean absolute WER reduction at -9 dB gain in experimental ASR systems.
This research provides a theoretical framework and practical solution for optimizing gain control in ASR to improve accuracy.