Topological Deep Learning for Speech Data
Topological data analysis (TDA) offers novel mathematical tools for deep learning. Inspired by Carlsson et al., this study designs topology-aware convolutional kernels that significantly improve speech recognition networks. Theoretically, by investigating orthogonal group actions on kernels, we establish a fiber-bundle decomposition of matrix spaces, enabling new filter generation methods. Practically, our proposed Orthogonal Feature (OF) layer achieves superior performance in phoneme recognition, particularly in low-noise scenarios, while demonstrating cross-domain adaptability. This work reveals TDA's potential in neural network optimization, opening new avenues for mathematics-deep learning interdisciplinary studies.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningPhoneme Recognitionspeech-recognitionSpeech RecognitionTopological Data AnalysisSimilar Papers 제목 키워드 기반
Topological Data Analysis for Speech Processing
We apply topological data analysis (TDA) to speech classification problems and to the introspection of a pretrained speech model, HuBERT. To this end, we introduce a number of topological and algebraic features derived f…
Topological Data AnalysisSpeech Recognition Transformers: Topological-lingualism Perspective
Transformers have evolved with great success in various artificial intelligence tasks. Thanks to our recent prevalence of self-attention mechanisms, which capture long-term dependency, phenomenal outcomes in speech proce…
speech-recognitionSpeech RecognitionSurveyComplex Politics: A Quantitative Semantic and Topological Analysis of UK House of Commons Debates
This study is a first, exploratory attempt to use quantitative semantics techniques and topological analysis to analyze systemic patterns arising in a complex political system. In particular, we use a rich data set cover…
Dynamic Topic ModelingTopological Data AnalysisTopological Data Mapping of Online Hate Speech, Misinformation, and General Mental Health: A Large Language Model Based Study
The advent of social media has led to an increased concern over its potential to propagate hate speech and misinformation, which, in addition to contributing to prejudice and discrimination, has been suspected of playing…
Language ModelingLanguage ModellingLarge Language ModelMisinformation+1Information-Geometric Superposed Vowel Evaluation: Part 1. Moraic Syllabary (Japanese)
This paper explains the principles and provides examples of a new method for distinguishing between FAKE human speech synthesized by generative AI and natural speech. Since synthetic speech is generated based on informat…