Research on speech recognition technology based on deep learning
With the rapid development of artificial intelligence technology, speech recognition technology has become one of the hot spots of people's attention. Speech recognition technology is a technology that converts speech signals into text form, which can be widely used in voice interaction, intelligent customer service, voice translation, voice search and other fields. In the implementation of speech recognition technology, speech recognition technology based on deep learning has become the mainstream.
Deep learning is a machine learning technology modeled on the cognitive process of the human brain, which has outstanding performance in the field of big data analysis and processing such as image, speech, and natural language processing.
Traditional speech recognition technology uses machine learning, statistics and other methods, its main limitation is that the feature extraction and pattern matching ability of speech signals is limited, often need to manually select features, through manual rules processing, resulting in mixed speech signals and complex speech environment adaptability is not strong.
Deep learning technology has stronger adaptive ability and generalization ability, and can use end-to-end method for speech signal processing and feature extraction, which greatly improves the accuracy and efficiency of speech recognition.
Among deep learning algorithms, Deep Neural Networks (DNNs) are the most commonly used of them. Deep learning-based speech recognition technology is mainly achieved by using a method called "end-to-end learning". Such a method can directly match the speech signal with the corresponding text information without the need for complex processes such as voice signal feature extraction.
In order to be able to better implement deep learning-based speech recognition technology, researchers often need to collect a large amount of speech data. This data is used as the basis for model training, and the accuracy of the model is gradually improved by repeatedly training the model.
With the continuous accumulation of training data and the continuous optimization of model algorithms, the recognition accuracy of speech recognition technology based on deep learning has been very high, which can reach an accuracy rate of more than 98%.
Speech recognition technology based on deep learning is a very popular direction in recent years, and its core technology is to use deep learning models to extract and classify the features of sound signals. The specific process is as follows:
Converts the sound signal to the Mel frequency cepstral coefficient (MFCC). MFCC is a commonly used technique for speech signal processing and feature extraction, through which sound signals can be converted into a set of meaningful feature vectors to provide a basis for subsequent feature classification.
MFCC features are processed using convolutional neural networks (CNNs) to obtain higher-level feature representations.
Recurrent neural networks (RNNs) such as long short-term memory networks (LSTMs) or gated recurrent units (GRUs) are used to model and batch process the output of CNNs to extract the linguistic features of speech signals.
Implementing deep learning-based speech recognition technology requires the following steps:
Data preparation: When performing speech recognition tasks, a large number of speech datasets are required to train the model. You can use public voice datasets including Librispeech, TIMIT, etc., or you can use your own voice datasets.
Speech preprocessing: Uses techniques such as Mel-frequency cepstral coefficients (MFCCs) and some frequency domain filters to extract feature vectors from raw audio data to provide input to deep learning models.
Model construction: Deep learning models are built based on frameworks such as Keras and Tensorflow, and convolutional neural networks (CNNs), recurrent neural networks (RNNs) and attention mechanisms can be used for speech signal processing and feature extraction.
Model training: Use the prepared dataset to train the model, use the training set for preliminary training, and then use the validation set for tuning to avoid overfitting.
Model evaluation: Use test sets to evaluate the accuracy and efficiency of the model.
Application deployment: Integrate the model into the actual application, conduct online testing and adjustment, and finally realize the application of speech recognition function.
Figure 1, Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9 is a speech recognition technology code example based on deep learning.
Compared with traditional speech recognition technology, speech recognition technology based on deep learning has higher accuracy and greater application potential.
In the future, speech recognition technology based on deep learning will continue to improve the recognition accuracy and continue to expand the application field. In the application fields of the Internet of Things, smart home, artificial intelligence and so on, speech recognition technology based on deep learning will gradually replace traditional manual operation and become one of the main means of human-computer interaction.
Therefore, speech recognition technology based on deep learning has become an important direction of current artificial intelligence research, and the future development potential is very broad. With the continuous improvement of intelligence level, speech recognition technology based on deep learning will be more widely used and developed in the future.
