Noise as an unwanted interference can significantly degrade speech signals, especially those recorded by many microphones. This interference is modeled as additive noise that originates from a range of sources including White Gaussian Noise (WGN), babble, crowd, large city, and traffic noises. These disturbances can alter the characteristics of speech signals reducing both their quality and intelligibility. This paper introduces a novel approach designed to reduce noise and enhance the quality and intelligibility of speech signals. The proposed method combines Wavelet Transform with Adaptive Filters, specifically the Wiener filter and RLS filter. The evaluation process involves testing noisy speech signals under realistic conditions with different signal-to-noise ratios (SNRs) and different types of additive noise. The objective measure is used for evaluation, including the perceptual evaluation of speech quality (PESQ). Results show that combining Wiener or RLS filtering with Wavelet Transform significantly improves noise reduction, outperforming the use of Wavelet Transform alone.

In modern communication, speech technology plays a vital role, with accent being one of the key features that reflects the nuances of spoken technology. However, differences in accent can make communication difficult, especially in global contexts like language learning and human computer interaction. Accent conversion approaches often solve these issues by transforming the accent of speaker to that of the other speakers, while maintaining linguistic information and speaker identity. This technology holds great applications to speech recognition, language learning, and cross-lingual communication. This paper explores the use of advanced deep learning techniques in accent conversion systems which have improved both the naturalness and intelligibility of converted speech. Additionally, we discuss the methods generally used for performance evaluation of these systems.

A necessary capability for humanoid robots is the ability to stand and walk while rejecting natural disturbances. Recent progress has been made using sim-to-real reinforcement learning (RL) to train such locomotion controllers, with approaches differing mainly in their reward functions. However, prior works lack a clear method to systematically test new reward functions and compare controller performance through repeatable experiments. This limits our understanding of the trade-offs between approaches and hinders progress. To address this, we propose a low-cost, quantitative benchmarking method to evaluate and compare the real-world performance of standing and walking (SaW) controllers on metrics like command following, disturbance recovery, and energy efficiency. We also revisit reward function design and construct a minimally constraining reward function to train SaW controllers. We experimentally verify that our benchmarking framework can identify areas for improvement, which can be systematically addressed to enhance the policies. We also compare our new controller to state-of-the-art controllers on the Digit humanoid robot. The results provide clear quantitative trade-offs among the controllers and suggest directions for future improvements to the reward functions and expansion of the benchmarks.

Supervised and Unsupervised ML algorithms are widely used over graphs. They use the structural properties of the data to deliver effective results. It is known that the same information can be represented under various graph structures. Thus, these algorithms may be effective on some structural variations of the data and ineffective on others. One would like to have an algorithm that is effective and generalizes to all structural variations of a data graph. We define the concept of structural generalizability for algorithms over graphs. We focus on the problem of similarity search, which is a popular task and the building block of many ML algorithms on graphs, and propose a structurally generalizable similarity search algorithm. As this algorithm may require users to specify features in a rather complex language, we modify this algorithm so that it requires only simple guidance from the user. Our extensive empirical study show that our algorithms are structurally generalizable while being efficient and more effective than current algorithms.