WO2021062705A1 - 一种单声道鲁棒性的语音关键词实时检测方法 - Google Patents
一种单声道鲁棒性的语音关键词实时检测方法 Download PDFInfo
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/02—Feature extraction for speech recognition; Selection of recognition unit
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- G10L15/08—Speech classification or search
- G10L15/14—Speech classification or search using statistical models, e.g. Hidden Markov Models [HMMs]
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- the invention relates to the technical field of electronic communication noise reduction, and more specifically, to a method for real-time detection of monophonic robust speech keywords.
- the technical problem to be solved by the present invention is how to use a single-channel robust voice keyword real-time detection method to solve the problem that the method in the prior art is not robust to noise and has a high false alarm rate The problem.
- the technical solution adopted by the present invention to solve its technical problem is: using a single-channel robust voice keyword real-time detection method, which performs keyword detection on the electronic format voice collected by a single microphone, compared with beam For keyword detection of the formed microphone array, this method can still maintain a high wake-up rate without using spatial location information, and only uses one microphone, which has a wider range of application scenarios.
- This method uses a supervised learning method for keyword detection, and realizes a keyword detection method that is robust in noisy scenarios by combining two training targets of noise reduction and keyword detection. This method has excellent performance, can still maintain a high wake-up rate in a noisy environment, has a wider range of practicability, and can greatly reduce the false alarm rate of the neural network.
- the mono-robust real-time speech keyword detection method includes the following steps:
- S1 receives the noisy speech signal in electronic format, which contains human voice and non-human background noise
- S2 uses short-time Fourier transform frame by frame to convert time-domain noisy speech signals into frequency-domain signals
- S3 uses the Mel filter to process the frequency domain signal to obtain the Mel feature and use it as an acoustic feature
- S4 neural network includes: convolutional neural network, one-way long and short-term memory regression neural network and feedforward deep neural network;
- the Mel feature passes through the convolutional neural network, the one-way long-short-term memory regression neural network and the feedforward deep neural network frame by frame, it is processed with the normalized exponential function to obtain the confidence information of each keyword;
- the output signal of the S6 neural network passes through the attention mechanism and the feedforward deep neural network in turn, and is processed by the normalized exponential function to obtain the confidence information of each keyword at the sentence level.
- the confidence value is greater than the predefined value Threshold, it is considered that the keyword is detected, otherwise it is considered that the keyword is not detected.
- the mel feature is formed by splicing the mel feature of the current frame and several future frames.
- the unidirectional long-short-term memory regression neural network includes a plurality of stacked unidirectional layers, and each unidirectional layer has 64 neurons .
- the neural network uses a large data set with noise for training, wherein the noisy speech is a mixture of multiple noises and multiple speaker voices;
- noisy speech is a mixture of thousands of different types of noise and the speech of more than 500 speakers.
- the convolutional neural network is formed by stacking a number of single convolutional layers
- Each single convolutional layer of the convolutional neural network is connected by an activation function layer.
- the feedforward deep neural network is formed by stacking multiple single linear layers
- Each linear layer of the feedforward deep neural network is connected by an activation function layer.
- the attention mechanism adopts a soft attention mechanism.
- the input of the attention mechanism comes from the output of the convolutional neural network layer.
- the input of the attention mechanism is obtained by mixing the output signals of the current frame and the convolutional layer of several past frames.
- the vector size of the output of the neural network is the number of keywords participating in training plus one.
- the beneficial effect is that the present invention provides a robust monophonic real-time detection method for speech keywords.
- the method has excellent performance and can effectively reduce call noise in a short-distance conversation scenario. Compared with the prior art, it has stronger practicability and does not depend on the noise and the speaker.
- Fig. 1 is a schematic block diagram of a mono-robust real-time voice keyword detection method of the present invention
- Fig. 2 is a corresponding table of the key words and serial numbers of the sample key words and serial numbers of a robust monophonic voice keyword real-time detection method of the present invention
- Figure 3 is a graph showing the downward trend of cross-entropy loss in a mono robust voice keyword real-time detection method of the present invention
- FIG. 4 shows the change trend of the mean square error in the real training process of a mono robust voice keyword real-time detection method of the present invention
- Fig. 5 is a schematic diagram of the realization of the soft attention mechanism of a mono-robust real-time speech keyword detection method of the present invention.
- Fig. 6 is a flow chart of a method for real-time detection of a monophonic robust voice keyword according to the present invention.
- a robust monophonic real-time detection method for speech keywords includes the following steps: S1 receives an electronic format noisy speech signal, which contains human voice and non-human background noise; S2 uses short-time Fourier transform to convert time-domain noisy speech signals into frequency-domain signals frame by frame; S3 uses Mel filters to process frequency-domain signals to obtain Mel features and use them as acoustic features; S4 Neural networks include: convolutional neural network, one-way long and short-term memory regression neural network and feedforward deep neural network; Mel features go through the convolutional neural network frame by frame, one-way long and short-term memory regression neural network and feedforward deep neural network After the network, use the normalized exponential function (Softmax) to obtain the confidence information of each keyword; S5 When the confidence of a certain keyword is greater than the predefined threshold, take the current frame and move forward several frames Spliced and used as the output signal of the neural network; the output signal of the S6 neural network passes through the attention mechanism and the feedforward deep neural network in turn, and is processed by
- confidence is also called reliability, or confidence level, confidence coefficient, that is, when sampling to estimate the overall parameters, the conclusion is always uncertain due to the randomness of the sample. Therefore, a probability statement method is adopted, that is, the interval estimation method in mathematical statistics, that is, the estimated value and the overall parameter are within a certain allowable error range, and the corresponding probability is called the confidence.
- short-time Fourier transform is a mathematical transformation related to Fourier transform, used to determine the frequency and phase of a sine wave in a local area of a time-varying signal.
- the normalized exponential function or Softmax function in mathematics, especially probability theory and related fields, is a kind of extension of the logic function. It can "compress" a K-dimensional vector z containing any real number into another K-dimensional real vector ⁇ (z), so that the range of each element is between (0,1), and the sum of all elements is 1. This function is more than in multi-classification problems.
- step S2 the time-domain digital signal is converted into a frequency-domain signal by Fast Fourier Transform (FFT). After the conversion into a frequency-domain signal, we can easily analyze the frequency components of the signal. To perform processing on the above, many signal processing algorithms that cannot be completed in the time domain can be realized.
- FFT Fast Fourier Transform
- the noisy speech training process in step S1 is a mixture of pure speech and noises with different signal-to-noise ratios
- the real collected speech is used in the inference process
- the feature extraction in step S2 Framing and windowing the noisy speech, each frame is 20 milliseconds in length, and there is an overlap of ten milliseconds between adjacent frames.
- the fast Fourier transform is used to extract the spectral amplitude vector on each frame, and then the Mel filter is used for filtering to obtain the acoustic characteristics of each frame. Because the speech signal has a large correlation in the time dimension, and this correlation is of great help to the task of keyword detection.
- this method splices the current frame and several frames before and after it into a vector with a larger dimension as the input feature.
- the method is executed by a computer program to extract acoustic features from noisy speech, and use a deep neural network for processing to obtain whether the original noisy speech contains keywords.
- the method includes one or more program modules, and any system or hardware device with executable computer programming instructions executes the one or more modules mentioned above.
- the main reason is that the long and short-term memory regression neural network is used as a component of the network in the present invention.
- the long and short-term memory regression neural network has the function of retaining part of important historical input information. Therefore, there is no need to splice historical frames, and only rely on future frames, which can reduce the amount of calculation of the neural network and the power consumption of the hardware devices that it depends on.
- the present invention supports one or more keywords. Different numbers of wake-up words correspond to different dimensions of network output.
- the network output includes the first-level frame-level output and the second-level sentence-level output.
- the network output dimension is equal to the number of keywords plus one. For example, if there is one keyword, the output dimension of the network is 2, and if there are two keywords, the output dimension of the network is 3, and so on.
- each command word should be numbered. For example, there are the following six keywords "turn on the light”, “turn on the TV”, “turn on the air conditioner”, “turn off the light”, “turn off the TV”, “turn off the air conditioner” ", can be numbered as shown in Figure 2.
- non-keywords are numbered as 0, and the keywords are numbered sequentially from 1 as the labels of the speech used for training. .
- the label should be one-hot encoding, and then the result of this encoding should be combined with the neural network output to obtain the cross entropy loss (Cross Entropy Loss).
- cross entropy is an important concept in Shannon's information theory, which is mainly used to measure the difference information between two probability distributions.
- the performance of a language model is usually measured by cross-entropy and perplexity.
- the meaning of cross entropy is the difficulty of text recognition with this model, or from the point of view of compression, each word needs to be coded with several bits on average.
- the meaning of complexity is to use the model to represent the average number of branches of this text, and the reciprocal can be regarded as the average probability of each word.
- Generalization ability refers to the performance of the method in a scenario where the training is not involved.
- the generalization performance of the method in the present invention mainly uses non-noise vocal speech and noise collected from about 10,000 different scenes to mix noise-added speech with different signal-to-noise ratio (SNR) and different loudness, Then solve the generalization problem through large-scale training. Since the regression neural network has the ability to model the long-term dependence of the signal, the proposed model has good generalization for new noise and speaker scenes, which is very important for practical applications.
- the present invention uses an RNN model that relies on future frames, so that the network can obtain information from the past and the future.
- the input is a noisy speech signal and the output keyword is labeled.
- the "1" in the figure indicates that it is involved during training.
- "2" represents the step of inference or prediction phase
- "3" in the figure represents the step shared by training and prediction.
- IRM ideal ratio membrane
- the present invention uses a 7-layer deconvolutional neural network (De-CNN) corresponding to a 7-layer convolutional neural network (CNN) to estimate the ideal ratio of each input noisy speech, and then calculates The mean square error (MSE) of the ideal ratio film and the estimated ratio film.
- the loss function of the neural network includes both cross-entropy loss and mean square error.
- the neural network minimizes the loss function of the entire training set through repeated iterations. After the training phase is over, enter the prediction phase, in which the deconvolution part is not used at all. This training method of training multiple targets at the same time is generally called joint training.
- the output of the deconvolutional neural network part is the predicted ideal ratio film.
- the ideal ratio film and the mel feature of noisy speech can be used to restore the mel feature of pure speech, so the predicted ideal ratio film is added as the training target It can make the convolutional neural network part can retain more pure voice features, filter out noise features that are not related to keyword detection, can make the entire neural network have better noise reduction performance, and make the present invention robust in a strong noise environment It is highly accurate and can still obtain a high accuracy rate in a strong noise environment.
- MSE mean square error
- the robustness means that the wake-up system can still be waked up correctly and maintain a low false wake-up rate even in the case of noise interference or target voice changes.
- the mean-square error (MSE) value is decreasing rapidly from about 1200 times to about 1400 times.
- the cross-entropy loss value is also rapidly decreasing, which can be seen as The performance of the noise reduction task is improved, and the performance of the keyword detection task is also continuously improved, indicating that the performance of the noise reduction task has a synergistic effect on the performance of the keyword detection task.
- the training process of the present invention there are two different stages of training process.
- the first stage is the aforementioned joint training mean square error (MSE) and cross entropy loss.
- the second stage training process mainly trains sentence-level keyword detection.
- the trained 7-layer convolutional neural network has a good noise reduction effect, so in this stage of training, the weight parameters of this part of the convolutional neural network are frozen, making it here Does not update during phase training.
- the current frame is spliced with 179 historical frames and a total of 180 frames.
- the output of the convolutional neural network is used as the input of the attention mechanism. These 180 frames correspond to 1.8 seconds of speech.
- the attention mechanism is derived from the study of human vision. In cognitive science, due to the bottleneck of information processing, humans will selectively pay attention to part of the information while ignoring other visible information. The attention mechanism can be used here from a The keywords extracted from the longer time series detect relevant information while ignoring other useless information. Using the second-level wakeup with attention mechanism can effectively avoid the problem of poor performance caused by the high false alarm rate caused by only the first-level wakeup.
- the attention mechanism used in the present invention is a soft attention mechanism.
- T represents the length of the time sequence used by the attention mechanism
- ht represents the input of the attention mechanism at time t
- ⁇ t represents the weight corresponding to ht at time t
- ct represents the output of the attention mechanism at the previous T frame.
- the process of the entire attention mechanism is to first calculate the output et of the hidden state of each frame of the previous T frames, and then use the normalized exponential function to calculate these hidden states as the weight of each frame, and finally the entire sequence of events
- the weighted sum of the input of each frame is the output of the attention mechanism.
- the mel feature is formed by concatenating the mel feature of the current frame and several future frames.
- the fast Fourier transform is used to extract the spectral amplitude vector on each frame, and then the Mel filter is used for filtering to obtain the acoustic feature of each frame, that is, the Mel feature.
- the unidirectional long- and short-term memory regression neural network includes multiple stacked unidirectional layers, and each unidirectional layer has 64 neurons.
- the neural network is trained using a large data set with noise, where the noisy speech is a mixture of multiple noises and multiple speaker voices;
- noisy speech is a mixture of thousands of different types of noise and the speech of more than 500 speakers.
- the convolutional neural network is formed by stacking several single convolutional layers; each single convolutional layer of the convolutional neural network is connected by an activation function layer.
- a convolutional neural network is a deep feedforward artificial neural network. Artificial neurons can respond to surrounding units.
- the convolutional neural network includes a convolutional layer and a pooling layer.
- the feed-forward deep neural network is formed by stacking multiple single-layer linear layers; each linear layer of the feed-forward deep neural network is connected by an activation function layer.
- a feedforward deep neural network (Deep Neural Networks, DNN) is a type with at least one hidden layer, which uses an activation function to de-linearize, uses cross entropy as a loss function, and uses a back propagation optimization algorithm (stochastic gradient Descent algorithm, batch gradient descent algorithm) is a feedforward neural network for learning and training (adjusting and updating the weights between neurons).
- the attention mechanism uses a soft-attention mechanism.
- the input of the attention mechanism comes from the output of the convolutional neural network layer.
- the input of the attention mechanism is obtained by mixing the output signals of the current frame and several past frame convolutional layers.
- the attention mechanism can expand the capabilities of neural networks and allow approximation of more complex functions. In more intuitive terms, it can focus on the characteristic part of the input, which can help improve the benchmark performance of natural language processing and also bring images. New capabilities such as description, memory network addressing, and neural programmers.
- the vector size of the output of the neural network is the number of keywords participating in training plus one.
- the mono-robust keyword detection of the present invention refers to the signal collected by a single microphone. Compared with the keyword detection of a beam-forming microphone array, the mono-keyword detection has a wider range of practicability.
- the present invention adopts a supervised learning method for keyword detection, and detects keywords through a convolutional neural network, a long and short-term memory regression neural network, and a feedforward deep neural network.
- the present invention adopts sentence-level attention mechanism and feedforward deep neural network as the second level of neural network, and based on convolutional neural network, long and short-term memory regression neural network and feedforward deep neural network as the first level After the output confidence of is greater than the threshold, the second-level network is used for confirmation.
- LSTM Long Short-Term Memory Regression Neural Network
- RNN recurrent neural network
- the robustness of the monophonic robust wake-up word detection of the present invention is reflected in that it can maintain a high wake-up rate in a noisy environment, and can maintain a high wake-up rate of 90% under a noisy wake-up with a signal-to-noise ratio of 0dB % Above the correct wake-up rate.
- the signal-to-noise ratio (SNR, S/N) is also called the signal-to-noise ratio.
- SNR signal-to-noise ratio
- the signal here refers to the electronic signal from the outside of the device that needs to be processed by this device
- the noise refers to the irregular extra signal (or information) that does not exist in the original signal generated after passing through the device. The signal does not change with the change of the original signal.
- robust single-channel keyword detection refers to keyword detection of the electronic format voice collected by a single microphone.
- the present invention can be used without spatial location information. In this case, a high wake-up rate is still maintained, and only one microphone is used, which has a wider range of application scenarios.
- the invention adopts a supervised learning method for keyword detection, and realizes a keyword detection method with robustness in noise scenes by combining two training targets of noise reduction and keyword detection.
- the present invention introduces a second-level network to reduce the problem of excessively high false alarm rate of keyword detection, where the attention mechanism in the second-level network can extract keyword-related information from a longer time series.
- the second-level network executes its related logic only after the output of the first-level network is greater than the threshold, which can save part of the calculation cost.
- the false alarm rate is also called the false alarm probability, which refers to the probability that a target is judged to have a target when there is no target when the threshold detection method is adopted in the process of radar detection.
- the present invention provides a single-channel robust real-time detection method for speech keywords, which adopts the single-channel robust keyword detection method for signals collected by a single microphone, compared with the keyword detection of a beam-forming microphone array, It has a wider range of practicability, and adopts a second-level network for confirmation. At the cost of less impact on performance, it can greatly reduce the false alarm rate of the neural network, improve the performance, and can still work in noisy environments. Maintain a high wake-up rate.
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- 一种单声道鲁棒性的语音关键词实时检测方法,其特征在于,包括如下步骤:S1接收电子格式的带噪语音信号,其中包含了人声语音和非人声的背景噪音;S2逐帧用短时傅里叶变换把时域的所述带噪语音信号转化为频域信号;S3使用梅尔滤波器对所述频域信号进行处理,得到梅尔特征并将其处作为声学特征;S4神经网络包括:卷积神经网络、单向长短期记忆回归神经网络和前馈型深度神经网络;所述梅尔特征逐帧经过卷积神经网络、单向长短期记忆回归神经网络和前馈型深度神经网络后,再用归一化指数函数处理后得到每个关键词的置信度信息;S5当某一个所述关键词的置信度大于预先定义的阈值之后则取当前帧和往前推移若干帧拼接,并作为所述神经网络的输出信号;S6所述神经网络的输出信号依次通过注意力机制与所述前馈型深度神经网络,并经过所述归一化指数函数处理后,得到句子级别的每个所述关键词的所述置信度信息,当所述置信度数值大于预先定义的阈值,则认为检测到所述关键词,否则视为没有检测到所述关键词。
- 根据权利要求1所述的一种单声道鲁棒性的语音关键词实时检测方法,其特征在于,所述梅尔特征是由当前帧梅尔特征和未来若干帧拼接而成。
- 根据权利要求1所述的一种单声道鲁棒性的语音关键词实时检测方法, 其特征在于,所述单向长短期记忆回归神经网络包含多个堆叠的单向层,每个所述单向层具有六十四个神经元。
- 根据权利要求1所述的一种单声道鲁棒性的语音关键词实时检测方法,其特征在于,所述神经网络采用带噪音大数据集进行训练,其中带噪语音由多种噪音和多个说话人语音混合而成;所述带噪语音由数千种不同类型的噪音和五百个以上的说话人的语音混合而成。
- 根据权利要求1所述的一种单声道鲁棒性的语音关键词实时检测方法,其特征在于,所述卷积神经网络是由若干个单卷积层堆叠而成;所述卷积神经网络的每个所述单卷积层通过激活函数层相连接。
- 根据权利要求1所述的一种单声道鲁棒性的语音关键词实时检测方法,其特征在于,所述前馈型深度神经网络由多个单层线性层堆叠而成;所述前馈型深度神经网络的每个线性层之间通过激活函数层相连接。
- 根据权利要求1所述的一种单声道鲁棒性的语音关键词实时检测方法,其特征在于,所述注意力机制采用的是软注意力机制。
- 根据权利要求1所述的一种单声道鲁棒性的语音关键词实时检测方法,其特征在于,所述注意力机制的输入来自于卷积神经网络层的输出。
- 根据权利要求1所述的一种单声道鲁棒性的语音关键词实时检测方法,其特征在于,所述注意力机制的输入是将当前帧和若干个过去帧卷积层的输出信号混合所得。
- 根据权利要求1所述的一种单声道鲁棒性的语音关键词实时检测方法,其特征在于,所述神经网络的输出的向量大小为参与训练的所述关键词的 数量加一。
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