Author: Zen and the Art of Computer Programming
1 Introduction
In fields such as computer vision, natural language processing, and speech recognition, deep learning technology has been in a vigorous development stage. In recent years, with the emergence of end-to-end deep learning systems, there are more and more human-computer interaction applications based on deep learning. Automatic speech recognition (ASR) is one of the important subfields, which uses deep learning methods to solve speech recognition problems. This article introduces how to build an ASR system using deep learning, and discusses its advantages, limitations, and future development directions.
2. Explanation of basic concepts and terms
2.1 Basics of deep learning
First, understanding the basic concepts and terminology related to deep learning will help us understand the technology mentioned in this article. Below are some important concepts and terms.
2.1.1 Deep learning
Deep learning is a branch of machine learning that simulates human neuron networks through multi-layer neural network models to achieve the ability to learn complex data. Deep learning can automatically extract data features and abstractly represent them, thereby helping computers better understand input data and make appropriate decisions. The key to deep learning is to use multi-layer neural networks to build learning models, rather than rule-based or statistical methods. Deep learning usually consists of two parts:
- Data representation (Representation): The deep learning model needs to convert the original data into a vector form in a high-dimensional space. This vector is usually composed of many unordered basis functions and can learn the intrinsic relationship of the data.
- Model training (Training): The deep learning model continuously updates parameters iteratively through the back propagation algorithm, so that the model approaches the true value and achieves the goal of prediction effect.
The reason why deep learning can achieve such results is that a multi-layer structure is introduced in the learning process, which can effectively extract data features and abstractly represent them. Deep learning is currently widely used in many fields, such as images, text, speech, reinforcement learning, etc.
2.1.2 Activation function
Activation function is a nonlinear function mainly used to control the value range of neuron output. Commonly used activation functions include sigmoid, tanh, ReLU, softmax, etc. Each layer of the deep learning model uses different activation functions, and these activation functions will affect the performance of the model. According to the requirements of different tasks, choosing an appropriate activation function is also an important consideration in designing a deep learning model.
2.1.3 Weight initialization
Weight initialization refers to assigning initial values to the weights in the neural network. Deep learning model training often requires a large amount of computing resources, so if the weights are too small or too large, it may lead to unstable model training or slow convergence. Therefore, before training, the weights need to be properly initialized to ensure the stability of model training. Common weight initialization methods include random initialization, zero-mean initialization, and Xavier/Glorot initialization.
2.1.4 Regularization
Regularization is a way to prevent overfitting. In machine learning, it is generally believed that the simpler the model, the easier the data will be disturbed by noise, and the performance of the model will become worse; on the contrary, the more complex the model, the more complex the data will be, and the performance of the model will not be affected by the data. In order to reduce the complexity of the model, we can use regularization to limit the complexity of the model, that is, reduce the number of model parameters or avoid overfitting of the model. Common regularization methods include L1 regularization, L2 regularization, Dropout, Early Stopping, Batch Normalization, etc.
2.1.5 Regression and classification problems
Regression problem and classification problem are two common problems in machine learning. The regression problem is the problem of predicting continuous values, such as predicting continuous value variables such as house prices and sales; the classification problem is the problem of predicting discrete values, such as predicting whether the user clicks on an advertisement, whether the text belongs to a specific category, etc. The difference between the two problems lies in the type of target variable. The target of the regression problem is a continuous value variable, and the target of the classification problem is a discrete value variable.
2.1.6 Loss function
The loss function is a measure of the prediction error of the model. During the training process of the deep learning model, the parameters of the model need to be adjusted based on the error between the model's prediction results and the actual labels to improve the performance of the model. Common loss functions include square loss, absolute value loss, Hinge loss, cross-entropy loss, etc.
2.1.7 Optimizer
Optimizer is used to control the way model parameters are updated. During the training process of the deep learning model, due to the large size of the training samples, there are many model parameters, and updating these parameters will take a long time. Therefore, effective methods are needed to control the update of model parameters. Common optimizers include SGD, Momentum, Adagrad, Adadelta, RMSprop, Adam, etc.
2.2 Convolutional Neural Network CNN
CNN (Convolutional Neural Network) is a commonly used model in deep learning that can effectively extract image features. The characteristic of CNN is to extract high-level features without changing the size of the input image. The most commonly used ones in CNN are the convolutional layer and the pooling layer, as shown in the figure below: The convolutional layer of CNN is responsible for learning local features of the image, such as edges, angles, etc.; the pooling layer further processes the previously obtained features, and further Reduce feature map size. The entire CNN model consists of multiple convolutional layers and pooling layers, and finally outputs the classification results through the fully connected layer.
2.3 RNN (LSTM) and attention mechanism
RNN (Recurrent Neural Networks) is a special neural network structure that can store information and obtain previous information at subsequent moments. In speech recognition systems, RNN can extract useful features and model feature sequences. RNN performs well on language modeling tasks because it can capture the relationship between adjacent words. The LSTM (Long Short Term Memory) structure is a commonly used RNN unit structure, which can memorize historical information for a long time and is suitable for speech recognition systems. Attention mechanism is an attention mechanism that helps RNN obtain input. The attention mechanism assigns different degrees of attention to the output at each time step, which can help RNN better focus on the input that currently needs to be processed.
3. Overview of automatic speech recognition
3.1 Speech recognition system flow chart
The flow chart of the speech recognition system is shown in the figure. First, the input signal is converted into a digital signal through the microphone, and then undergoes processes such as pre-emphasis, framing, and zero-crossing detection; then, power spectrum analysis is performed on each frame signal to extract speech features, and windowing and inverting are performed on the features. Spectrum, downsampling and other processing; then, the speech features are encoded to generate dense contextual features; finally, the features are evaluated using language models and acoustic models to determine the final recognition results.
3.2 Development process
In the early years, speech recognition systems were based on manually designed rules, but with the development of technology, speech recognition systems can be automated. With the development of deep learning technology, speech recognition systems also use deep learning methods. Early speech recognition systems mainly relied on short-time Fourier transform (STFT), which lacked time variability and spatial correlation; while modern speech recognition systems are based on convolutional neural networks (CNN) to extract speech features, and also use deep belief networks (DNN). ) as an acoustic model and a language model.
4. Use deep learning to build an automatic speech recognition system
4.1 Data preparation
First, collect a batch of voice data, including the speaker's recording files and corresponding text. This data is used to train the model. Common data formats include wav, mp3, etc. For the convenience of training, the data can be cleaned, cut, and split, but at the same time, attention should also be paid to collecting enough data. Additionally, additional data needs to be produced for testing so that the performance of the model can be evaluated.
4.2 Feature extraction
Speech features are the input of deep learning models, and some signal processing methods are usually used, such as filtering, pre-emphasis, windowing, cepstrum, etc. Common feature extraction methods are:
MFCC (Mel Frequency Cepstral Coefficients): MFCC is a fast Fourier Transform of the speech signal. Speech feature extraction is performed through Mel-Frequency Cepstral Coefficients (MFC). MFC represents the energy of each frequency of sound and can capture changes in tone, emotion, intonation, etc. in speech.
Filter bank: Filter bank refers to extracting the energy of different frequency ranges in the speech signal and combining them to form a series of sub-bands.
DNN Features: Commonly used speech features include Mel-filterbank energy (MFEE), log mel-spectrogram power (LOG_MELSPEC), and chroma features (CHROMA).
Statistical Features: Statistical features include speaking speed, pronunciation intensity, paragraph length, etc.
4.3 Data expansion
When the amount of data is relatively small, the amount of data can be increased through data expansion. For example, copy, flip, rotate, or use image data from different angles and perspectives. Data augmentation also allows the model to better adapt to new data.
4.4 Building the model
Using deep learning methods to build a speech recognition model can be divided into three steps:
Data preprocessing: First, preprocess the data, including removing silence, removing noise, windowing, alignment, cropping, etc. Then, the speech features are normalized, such as de-averaging or normalizing.
Model construction: Construct a convolutional neural network (CNN), input feature maps in the time domain or frequency domain, and output text sequences. CNN has many structures. Here we use a common convolutional neural network, that is, several convolutional layers followed by several pooling layers.
Model training: train the CNN model to fit the training data as closely as possible. Here, the optimizer used is Adam, the loss function is cross-entropy, and the batch size is 32.
4.5 Evaluation model
After training the model, you can evaluate the model's performance. Commonly used evaluation indicators include accuracy, recall, and F1 score.
4.6 Test model
Finally, test the model's performance. Test data is generally smaller than training data, but it still needs to verify the generalization ability of the model.
4.7 Deployment model
Deploying a model refers to applying the model to the actual production environment, which usually requires optimizing and improving the model. First of all, it is necessary to optimize the hardware conditions and operating efficiency of the production environment, such as using GPU acceleration to reduce memory usage. Secondly, model transfer learning needs to be considered, which is to fine-tune the existing model structure to better adapt to new environments and data. Third, the security of the model also needs to be considered, such as using encryption to transmit model parameters, or using technologies such as firewalls.
5. Advantages, limitations and future development directions
5.1 Advantages
The accuracy of the model is high: under a certain amount of data, the accuracy of the speech recognition system can reach more than 90%.
The model is highly robust: By introducing a deep learning model, the model's robustness can be improved.
End-to-end training process: Deep learning models do not need to manually specify feature extraction and acoustic models, but directly train the entire system.
The model is highly interpretable: Because deep learning models are highly interpretable, you can intuitively understand how the model works.
5.2 Limitations
Small amount of training data: The amount of training data for the speech recognition system is limited, and there are problems such as data noise and uneven distribution.
The spatiotemporal characteristics are not obvious: Since the deep learning model only considers the audio signal at the feature level, it ignores the spatiotemporal characteristics.
The model has weak recognition ability for long sentences: There is a problem in the speech recognition system, that is, the recognition ability of long sentences is weak. Because deep learning models usually only consider the characteristics of a single word or phrase, they cannot extract contextual features of long sentences.
5.3 Future development direction
Improving pronunciation recognition models using deep learning methods: Existing pronunciation recognition models often require hand-designed feature extraction and acoustic models. Deep learning methods can be used to automatically extract phoneme features, thereby improving the performance of pronunciation recognition models.
Introducing the attention mechanism to improve the recognition ability of long sentences: The attention mechanism can help the deep learning model better capture the contextual features of long sentences.
Provide server-side services: Speech recognition systems need to process a large amount of speech data, so server-side services will make model training and deployment more reliable.