Noise reduction is a critical aspect of hearing aids that researchers trying to solve over the years. Most of the noise reduction algorithms are evaluated using English Speech Material. There are many differences between the linguistic features of English and Sinhala languages, such as different syllable structures and different vowel duration. Both wavelet transformation and adaptive filtering have been widely used for noise reduction in hearing aids. This paper compares the performance of wavelet transformation of ten wavelet families with soft and hard thresholding methods against adaptive filters with Normalized Least Mean Square (NLMS), Least Mean Square (LMS), Average Normalized Least Mean Square (ANLMS), Recursive Least Square (RLS), and Adaptive Filtering Averaging (AFA) optimization algorithms along with cepstral and energy-based voice activity detection (VAD) algorithms. The performance evaluation is done using objective metrics; Signal to Noise Ratio (SNR) and Perceptual Evaluation of Speech Quality (PESQ) and a subjective metric; Mean Opinion Score (MOS). The NOIZEUS database by the University of Texas, Dallas and a newly formed Sinhala language audio database were used for the evaluation.

翻译：噪声抑制是助听器领域多年来研究者试图解决的关键问题。目前多数降噪算法均使用英语语音材料进行评测。英语与僧伽罗语在语言特征上存在诸多差异，例如音节结构和元音时长的不同。小波变换与自适应滤波技术已广泛应用于助听器的噪声抑制。本文比较了十种小波族采用软阈值与硬阈值方法的小波变换性能，以及采用归一化最小均方（NLMS）、最小均方（LMS）、平均归一化最小均方（ANLMS）、递归最小二乘（RLS）和自适应滤波平均（AFA）优化算法的自适应滤波器性能，并与基于倒谱和能量的语音活动检测（VAD）算法进行对比。性能评估采用客观指标：信噪比（SNR）和语音质量感知评估（PESQ），以及主观指标：平均意见得分（MOS）。评测数据集采用德克萨斯大学达拉斯分校提供的NOIZEUS数据库及新构建的僧伽罗语语音数据库。