Machine learning, in numpy
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Updated
Oct 29, 2023 - Python
Machine learning, in numpy
Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras
🔉 spafe: Simplified Python Audio Features Extraction
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…
Audio feature extraction and classification
🔉 👦 👧Voice based gender recognition using Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture models (GMM)
Synchronize your subtitles using machine learning
❓声学键盘|脑洞大开:做一个能听懂键盘敲击键位的「玩具」,学习信号处理 / 深度学习 / 安卓 / Django。
A program for automatic speaker identification using deep learning techniques.
A simple audio feature extraction library
The human speaks a language with an accent. A particular accent necessarily reflects a person's linguistic background. The model defines accent based audio record. The result of the model could be used to determine accents and help decrease accents to English learning students and improve accents by training.
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.
🔉 👦 👧 👩 👨 Speaker identification using voice MFCCs and GMM
A Python library for computing the Mel-Cepstral Distance (Mel-Cepstral Distortion, MCD) between two inputs. This implementation is based on the method proposed by Robert F. Kubichek in "Mel-Cepstral Distance Measure for Objective Speech Quality Assessment".
A implementation of Power Normalized Cepstral Coefficients: PNCC
基于DTW与MFCC特征进行数字0-9的语音识别,DTW,MFCC,语音识别,中英数据,端点检测,Digital Voice Recognition。
Deep Learning model for lexical stress detection in spoken English
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