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음성 인식
음성 인식에는 오디오 신호에서 음성(예: 음성 명령)을 감지하고 식별하는 작업이 포함됩니다. 예제를 따라 특징 추출, 머신러닝, 딥러닝을 음성 인식 응용 분야에 적용하는 워크플로를 확인하십시오.
도움말 항목
- AI for Speech Command Recognition
Build, train, compress, and deploy a deep learning model for speech command recognition.
추천 예제
딥러닝을 사용한 음성 명령 인식
사전 훈련된 딥러닝 모델을 사용하여 스트리밍 오디오에서 음성 명령 인식을 수행합니다.
Spoken Digit Recognition with Wavelet Scattering and Deep Learning
Classify spoken digits using both machine and deep learning techniques. In the example, you perform classification using wavelet time scattering with a support vector machine (SVM) and with a long short-term memory (LSTM) network. You also apply Bayesian optimization to determine suitable hyperparameters to improve the accuracy of the LSTM network. In addition, the example illustrates an approach using a deep convolutional neural network (CNN) and mel-frequency spectrograms.
Voice Activity Detection in Noise Using Deep Learning
Perform batch and streaming voice activity detection (VAD) in a low SNR environment using a pretrained deep learning model.
Train Voice Activity Detection in Noise Model Using Deep Learning
Train a BiLSTM network to perform voice activity detection (VAD) in a low SNR environment.
Speech Emotion Recognition
Implement a simple speech emotion recognition system using a BiLSTM network.
Keyword Spotting in Noise Using MFCC and LSTM Networks
Identify a keyword in noisy speech using a deep learning network. In particular, the example uses a Bidirectional Long Short-Term Memory (BiLSTM) network and mel frequency cepstral coefficients (MFCC).
Keyword Spotting in Simulink
Use a pretrained deep learning model in Simulink® to identify a keyword in speech.
