用卷积神经网络实现高精度的印地语关键词识别。
Keyword spotting using convolutional neural network for speech recognition in Hindi
- 用CNN处理音频的梅尔频谱倒谱系数，实现关键词检测。
- 在4万条语音数据上达到91.79%准确率，适合设备端部署。
- 专为印地语用户查询定制，兼顾效率与个性化。
本研究探讨了在印地语语音识别领域中关键词检测（KWS）的应用，使用包含40,000个音频样本的数据集，采样率为44 kHz，平均每个样本持续1.9秒。目标是开发一种高效、面向用户的设备端关键词检测系统。通过特征工程将原始音频转换为梅尔频率倒谱系数（MFCCs），输入卷积神经网络（CNN）进行分类。实验对比多种CNN架构，在连续语音流中识别预定义关键词的有效性。基于CNN的方法在严格评估下取得91.79%的准确率，展现出优异性能，同时保证计算效率和用户个性化适配能力。
原文摘要 · Abstract (English)
In this study, we investigate the application of keyword spotting (KWS) in the domain of Hindi speech recognition, utilizing a dataset comprising 40,000 audio samples. With a sampling rate of 44 kHz and an average duration of 1.9 seconds per sample, we focus on developing an efficient on-device KWS system tailored for user-specific queries. Leveraging Convolutional Neural Networks (CNNs) for classification, we employ feature engineering techniques to convert raw audio recordings into Mel Frequency Cepstral Coefficients (MFCCs) as an input for our network. Our experiments encompass various CNN architectures, exploring their efficacy in identifying predefined keywords within the continuous speech stream. Our CNN-based approach achieves a commendable accuracy rate of 91.79% through rigorous evaluation, demonstrating promising performance while ensuring computational efficiency and user-specific customization in Hindi speech recognition.
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