My Docker scripts and Dockerfile for several frameworks.

Lyrics-to-audio-alignement system. Based on Machine Learning Algorithms: Hidden Markov Models with Viterbi forced alignment. The alignment is explicitly aware of durations of musical notes. The phonetic model are classified with MLP Deep Neural Network.

The Hidden Markov Model Toolkit (HTK) from University of Cambridge, with fixed issues.

HTK Toolkit with Linux 64 bit and Docker support

Browser for long audio recordings.

P2FA-based HMM state-level forced aligner

Minimal HTK for supporting HTK in Vietnamese.

Automatic speech recognition (ASR) for Indonesian language built by using HTK and Julius. Web interface is built using Node.js.

Python wrapper for the Cambridge Hidden Markov Model Toolkit

Digital Signal Processing: HMM Training and Testing with the HTK toolkit

Offer hwyluso creu Adnabod Lleferydd Cymraeg gyda HTK, IRSTLM, Julius a Docker | Welsh Speech Recognition with HTK, IRSTLM, Julius and Docker

Installation steps of HTK 3.5 under Ubuntu

Wrist Wash Gesture Recognition

Course assignments for LS621 (IIT Guwahati, Jan-May 2018)

Some useless thing for Speech Synthesis System

Speech Tester is a set of Python scripts conceived as an extension to HTK Automatic Speech Recognition system (Young et al., 2002). Speech Tester aims to help language researchers and engineers to measure the intelligibility of transformed audio signals. Here the termed “transform” may describe any process that alters a speech signal or its reception by a potential listener. For instance, any type of audio compression, noise background and/or reverberation, vocoding (to simulate cochlear implants), partial auditory loss, or any combination of these and other imaginable transformations. Intelligibility refers to the capacity of a listener to recognize a transformed signal. In the rest of this introduction we describe the motivation to develop Speech Tester to measure speech intelligibility.

Refactored-HTK codes

HTK Toolkit with Linux 64 bit and Docker support

Gesture Recognition using HMMs

HW03 of Speech Processing