Advanced Clustering Techniques for Speech Signal Enhancement: A Review and Metanalysis of Fuzzy C-Means, K-Means, and Kernel Fuzzy C-Means Methods

• URL: http://arxiv.org/abs/2409.19448v1
• Date: Sat, 28 Sep 2024 20:21:05 GMT
• Title: Advanced Clustering Techniques for Speech Signal Enhancement: A Review and Metanalysis of Fuzzy C-Means, K-Means, and Kernel Fuzzy C-Means Methods
• Authors: Abdulhady Abas Abdullah, Aram Mahmood Ahmed, Tarik Rashid, Hadi Veisi, Yassin Hussein Rassul, Bryar Hassan, Polla Fattah, Sabat Abdulhameed Ali, Ahmed S. Shamsaldin,
• Abstract summary: Speech signal processing is tasked with improving the clarity and comprehensibility of audio data in noisy environments. The quality of speech recognition directly impacts user experience and accessibility in technology-driven communication. This review paper explores advanced clustering techniques, particularly focusing on the Kernel Fuzzy C-Means (KFCM) method.
• Score: 0.6530047924748276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speech signal processing is a cornerstone of modern communication technologies, tasked with improving the clarity and comprehensibility of audio data in noisy environments. The primary challenge in this field is the effective separation and recognition of speech from background noise, crucial for applications ranging from voice-activated assistants to automated transcription services. The quality of speech recognition directly impacts user experience and accessibility in technology-driven communication. This review paper explores advanced clustering techniques, particularly focusing on the Kernel Fuzzy C-Means (KFCM) method, to address these challenges. Our findings indicate that KFCM, compared to traditional methods like K-Means (KM) and Fuzzy C-Means (FCM), provides superior performance in handling non-linear and non-stationary noise conditions in speech signals. The most notable outcome of this review is the adaptability of KFCM to various noisy environments, making it a robust choice for speech enhancement applications. Additionally, the paper identifies gaps in current methodologies, such as the need for more dynamic clustering algorithms that can adapt in real time to changing noise conditions without compromising speech recognition quality. Key contributions include a detailed comparative analysis of current clustering algorithms and suggestions for further integrating hybrid models that combine KFCM with neural networks to enhance speech recognition accuracy. Through this review, we advocate for a shift towards more sophisticated, adaptive clustering techniques that can significantly improve speech enhancement and pave the way for more resilient speech processing systems.

Related papers

• Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives [2.608119698700597]
  This review aims to comprehensively cover advancements in CI-based ASR and speech enhancement, among other related aspects. The review will delve into potential applications and suggest future directions to bridge existing research gaps in this domain.
  arXiv  Detail & Related papers  (2024-03-17T11:28:23Z)
• A unified multichannel far-field speech recognition system: combining neural beamforming with attention based end-to-end model [14.795953417531907]
  We propose a unified multichannel far-field speech recognition system that combines the neural beamforming and transformer-based Listen, Spell, Attend (LAS) speech recognition system. The proposed method achieve 19.26% improvement when compared with a strong baseline.
  arXiv  Detail & Related papers  (2024-01-05T07:11:13Z)
• Language-Oriented Communication with Semantic Coding and Knowledge Distillation for Text-to-Image Generation [53.97155730116369]
  We put forward a novel framework of language-oriented semantic communication (LSC) In LSC, machines communicate using human language messages that can be interpreted and manipulated via natural language processing (NLP) techniques for SC efficiency. We introduce three innovative algorithms: 1) semantic source coding (SSC), which compresses a text prompt into its key head words capturing the prompt's syntactic essence; 2) semantic channel coding ( SCC), that improves robustness against errors by substituting head words with their lenghthier synonyms; and 3) semantic knowledge distillation (SKD), that produces listener-customized prompts via in-context learning the listener's
  arXiv  Detail & Related papers  (2023-09-20T08:19:05Z)
• Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments [21.493664174262737]
  This paper describes noisy speech recognition for an augmented reality headset that helps verbal communication within real multiparty conversational environments. We propose a semi-supervised adaptation method that jointly updates the mask estimator and the ASR model at run-time using clean speech signals with ground-truth transcriptions and noisy speech signals with highly-confident estimated transcriptions.
  arXiv  Detail & Related papers  (2022-07-15T03:43:35Z)
• Discretization and Re-synthesis: an alternative method to solve the Cocktail Party Problem [65.25725367771075]
  This study demonstrates, for the first time, that the synthesis-based approach can also perform well on this problem. Specifically, we propose a novel speech separation/enhancement model based on the recognition of discrete symbols. By utilizing the synthesis model with the input of discrete symbols, after the prediction of discrete symbol sequence, each target speech could be re-synthesized.
  arXiv  Detail & Related papers  (2021-12-17T08:35:40Z)
• Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments [75.8001929811943]
  We introduce a realistic and challenging, multi-source and multi-room acoustic environment. We present an improved clustering control strategy that takes into account the variability of the acoustic scene. The proposed approach is optimized using clustering-based measures and validated via a network-wide classification task.
  arXiv  Detail & Related papers  (2021-06-07T14:51:39Z)
• Learning to Rank Microphones for Distant Speech Recognition [16.47293353050145]
  Empirical evidence shows that being able to select the best microphone leads to significant improvements in recognition. Current channel selection techniques either rely on signal, decoder or posterior-based features. We propose MicRank, a learning to rank framework where a neural network is trained to rank the available channels.
  arXiv  Detail & Related papers  (2021-04-06T22:39:30Z)
• Gated Recurrent Fusion with Joint Training Framework for Robust End-to-End Speech Recognition [64.9317368575585]
  This paper proposes a gated recurrent fusion (GRF) method with joint training framework for robust end-to-end ASR. The GRF algorithm is used to dynamically combine the noisy and enhanced features. The proposed method achieves the relative character error rate (CER) reduction of 10.04% over the conventional joint enhancement and transformer method.
  arXiv  Detail & Related papers  (2020-11-09T08:52:05Z)
• On the Use of Audio Fingerprinting Features for Speech Enhancement with Generative Adversarial Network [24.287237963000745]
  Time-frequency domain features, such as the Short-Term Fourier Transform (STFT) and Mel-Frequency Cepstral Coefficients (MFCC) are preferred in many approaches. While the MFCC provide for a compact representation, they ignore the dynamics and distribution of energy in each mel-scale subband. In this work, a speech enhancement system based on Generative Adversarial Network (GAN) is implemented and tested with a combination of AFP and the Normalized Spectral Subband Centroids (NSSC)
  arXiv  Detail & Related papers  (2020-07-27T00:44:16Z)
• Simultaneous Denoising and Dereverberation Using Deep Embedding Features [64.58693911070228]
  We propose a joint training method for simultaneous speech denoising and dereverberation using deep embedding features. At the denoising stage, the DC network is leveraged to extract noise-free deep embedding features. At the dereverberation stage, instead of using the unsupervised K-means clustering algorithm, another neural network is utilized to estimate the anechoic speech.
  arXiv  Detail & Related papers  (2020-04-06T06:34:01Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.