METHOD AND SYSTEM FOR DETECTING VOICE ACTIVITY IN NOISY CONDITIONS
A voice activity detection method includes: training one or more computerized neural networks having a denoising autoencoder and a classifier, wherein the training is performed utilizing one or more models including Mel-frequency cepstral coefficients (MFCC) features, Δ features, ΔΔ features, and Pitch features, each model being recorded at one or more differing associated predetermined signal to noise ratios; recording a raw audio waveform and transmitting the raw audio waveform to the computerized neural network; denoising the raw audio wave utilizing the denoising autoencoder; and determining whether the raw audio waveform contains human speech; extracting any human speech from the raw audio waveform.
1 . A voice activity detection method comprising:
training one or more computerized neural networks having a denoising autoencoder and a classifier, wherein the training is performed utilizing one or more models including Mel-frequency cepstral coefficients (MFCC) features, Δ features, ΔΔ features, and Pitch features, each model being recorded at one or more differing associated predetermined signal to noise ratios;
recording a raw audio waveform and transmitting the raw audio waveform to the computerized neural network;
denoising the raw audio wave utilizing the denoising autoencoder; and
determining whether the raw audio waveform contains human speech;
extracting any human speech from the raw audio waveform.
2 . The voice activity detection method of claim 1 ,
wherein the computerized neural network is a convolutional neural network.
3 . The voice activity detection method of claim 1 ,
wherein the computerized neural network is a deep neural network.
4 . The voice activity detection method of claim 1 ,
wherein the computerized neural network is a recurrent neural network.
5 . The voice activity detection method of claim 1 ,
wherein the classifier is trained utilizing one or more linguistic models.
6 . The voice activity detection method of claim 5 ,
wherein the classifier is trained utilizing a plurality of linguistic models.
7 . The voice activity detection method of claim 6 ,
wherein at least one linguistic model is VoxForge™.
8 . The voice activity detection method of claim 6 ,
wherein at least one linguistic model is AIShell.
9 . The voice activity detection method of claim 6 ,
wherein at least one linguistic model is VoxForge™; and
wherein at least one additional linguistic model is AISHELL.
10 . The voice activity detection method of claim 6 ,
wherein each linguistic model is recorded having a base truth, wherein each linguistic model is recorded at one or more of a plurality of pre-set signal to noise ratios.
11 . The voice activity detection method of claim 10 ,
wherein each linguistic model is recorded having a base truth, wherein each linguistic model is recorded at a plurality of pre-set signal to noise ratios.
12 . The voice activity detection method of claim 11 ,
wherein the plurality of pre-set signal to noise ratios range between 0 dB and 35 dB.
13 . The voice activity detection method of claim 6 ,
wherein the raw audio waveform is recorded on a local computational device, and wherein method further comprises a step of transmitting the raw audio waveform to a remote server, wherein the remote server contains the computational neural network.
14 . The voice activity detection method of claim 6 ,
wherein the raw audio waveform is recorded on a local computational device, and wherein the local computational device contains the computational neural network.
15 . The voice activity detection method of claim 14 ,
wherein the computational neural network is compressed.
16 . A voice activity detection system, the system comprising:
a local computational system, the local computational system comprising:
processing circuitry;
a microphone operatively connected to the processing circuitry;
a non-transitory computer-readable media being operatively connected to the processing circuitry;
a remote server configured to receive recorded wavelengths from the local computational system; the remote server having one or more computerized neural networks, a denoising autoencoder module, and a classifier module, wherein the computerized neural networks of the remote server are trained on a plurality of acoustic models, wherein each of the plurality of acoustic models represent a particular linguistic dataset recorded in one or more associated noise predetermined signal to noise ratios;
wherein the non-transitory computer-readable media contains instructions for the processing circuitry to perform the following tasks:
utilize the microphone to record raw audio waveforms from an ambient atmosphere;
transmit the recorded raw audio waveforms to the remote server; and
wherein the remote server contains processing circuitry configured to utilize the denoising autoencoder module to perform a denoising operation on the recorded waveform and utilize the classifier to classify the recorded wavelengths as speech or non-speech, extract the speech from the recorded raw audio waveforms, perform a speech-to-text operation, and transmit one or more extracted strings of speech characters back to the local computational system.
17 . The voice activity detection system of claim 16 ,
wherein the classifier is trained utilizing a plurality of linguistic models, wherein at least one linguistic model is VoxForge™ and at least one linguistic model is AIShell.
18 . A vehicle comprising a voice activity detection system, the system comprising:
a local computational system, the local computational system further comprising:
processing circuitry;
a microphone operatively connected to the processing circuitry;
a non-transitory computer-readable media being operatively connected to the processing circuitry;
one or more computerized neural networks including:
a denoising autoencoder module, and
a classifier module, wherein the computerized neural networks are trained on a plurality of acoustic models, wherein each of the plurality of acoustic models represent a particular linguistic dataset recorded in one or more associated noise predetermined signal to noise ratios;
wherein the non-transitory computer-readable media contains instructions for the processing circuitry to perform the following tasks:
utilize the microphone to record raw audio waveforms from an ambient atmosphere;
transmit the recorded raw audio waveforms to the one or more computerized neural networks; and
wherein at least one computerized neural network is configured to utilize the denoising autoencoder module to perform a denoising operation on the recorded waveform and utilize the classifier to classify the recorded wavelengths as speech or non-speech, extract the speech from the recorded raw audio waveforms, perform a speech-to-text operation, and transmit one or more extracted strings of speech characters back to the local computational system.
19 . The vehicle of claim 18 ,
wherein the classifier is trained utilizing a plurality of linguistic models, wherein at least one linguistic model is VoxForge™ and at least one linguistic model is AIShell; and
wherein the computational neural network is compressed.
20 . The vehicle of claim 18 ,
wherein the vehicle is one of an automobile, a boat, or an aircraft.