332 Open source projects that are alternatives of or similar to Pase

A React-Native Bridge for the Google Dialogflow (API.AI) SDK

Deep neural network based speech enhancement toolkit

VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network

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].

This repo summarizes the tutorials, datasets, papers, codes and tools for speech separation and speaker extraction task. You are kindly invited to pull requests.

Tensorflow 2.x implementation of the DTLN real time speech denoising model. With TF-lite, ONNX and real-time audio processing support.

RETIRED - OpenSTT is now retired. If you would like more information on Mycroft AI's open source STT projects, please visit:

WaveNet vocoder

Collection of EM algorithms for blind source separation of audio signals

A minimum unofficial implementation of the "A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement" (CRN) using PyTorch

PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models

Tracking the progress in non-autoregressive generation (translation, transcription, etc.)

Neural speaker recognition/verification system based on Kaldi and Tensorflow

Deep Learning-based Voice Conversion system

Implement Wave-U-Net by PyTorch, and migrate it to the speech enhancement.

A light weight neural speaker embeddings extraction based on Kaldi and PyTorch.

Emotion recognition by speech in android.

A modified version of Speech Signal Processing Toolkit (SPTK)

ConvNets for Audio Recognition using Google Commands Dataset

Discriminative Neural Clustering for Speaker Diarisation

PyTorch implementation of "A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."

Keras (tensorflow) implementation of SincNet (Mirco Ravanelli, Yoshua Bengio - https://github.com/mravanelli/SincNet)

iOS application for finding formants in spoken sounds

A implementation of Power Normalized Cepstral Coefficients: PNCC

Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding

Natural Language Processing to generate new speeches for the President of Turkey.

SincNet is a neural architecture for efficiently processing raw audio samples.

Open source audio annotation tool for humans™

A curated list of awesome Speaker Diarization papers, libraries, datasets, and other resources.

A neural network for end-to-end speech denoising

Speech recognition toolkit for the arduino

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习