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RAGHUDATHESH G P's repositories

ADSP_Tutorials

Advanced Signal Processing Notebooks and Tutorials

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Hindi-ASR-Challenge-iitm

🎯 Speech Recognition Challenge by Speech Lab - IIT Madras

NOASSERTION000

Coursera

These are my learning exercices from Coursera

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Speech_Feature_Extraction

Feature extraction of speech signal is the initial stage of any speech recognition system.

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Awesome-Speech-Enhancement

A tutorial for Speech Enhancement researchers and practitioners. The purpose of this repo is to organize the world’s resources for speech enhancement and make them universally accessible and useful.

MIT000

Speech_Signal_Processing_and_Classification

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

MIT000

aws3transcribe

AWS Transcribe and S3 buckets management code. Feel free to contribute or fork.

MIT000

audino

Open source audio annotation tool for humans™

MIT000

Students-Performance-Analytics

Students Performance Evaluation using Feature Engineering, Feature Extraction, Manipulation of Data, Data Analysis, Data Visualization and at lat applying Classification Algorithms from Machine Learning to Separate Students with different grades

GPL-3.0000

semetrics

Speech Enhancement Metrics (PESQ, CSIG, CBAK, COVL)

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Mesh-Networking-based-Home-Automation

This Repo contains the code for all the board which I used to show how you can make home automation using Mesh Networking

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ArduinoFreeRTOS

MSOISES

Language:C++000

open-speech-corpora

A list of accessible speech corpora for ASR, TTS, and other Speech Technologies

MIT000

cisco-packet-tracer-MSOIS-2019

Workshop Material scenario files, document and PPT

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ASR-System-for-Hindi-Language

The repository contains all the codes necessary for my project - Automatic Speech Recognition System in Hindi Language ( Project description is available at :- https://goo.gl/eQZkMP) : It containes the code for the following systems - 1) Monophone-HMM system built using HTK toolkit , 2)Monophone-HMM system built using Kaldi toolkit, 3)Triphone-HMM system built using Kaldi toolkit and 4)DNN-HMM system built using Kaldi toolkit

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