Accent differences in speech emotion recognition technology

The issue of accent differences in speech emotion recognition technology requires specific code examples

With the rapid development of speech recognition technology and artificial intelligence, speech emotion recognition has become a An area of ​​research that has attracted much attention. Accurately identifying the speaker's emotional state is of great significance to fields such as human-computer interaction and sentiment analysis. However, in practical applications, the difference in accents between different speakers leads to a decrease in emotion recognition performance. This article will discuss the issue of accent differences in speech emotion recognition and give specific code examples.

Accent refers to the specific phonetic characteristics presented by a speaker in pronunciation, and is the individual difference in pronunciation of language users. Different phonetic features are often related to the speaker's region, culture, native language and other factors. These differences can lead to difficulties in speech emotion recognition, because different accents may correspond to different emotional expressions. For example, people in some areas have a brisk rhythm in their pronunciation, while people in other areas have a slower and more steady pronunciation. This difference often affects the extraction and analysis of sound features by emotion recognition systems.

In order to solve the problem of accent differences, you can proceed through the following steps:

First, you need to create a training set with multiple accent samples. This training set should contain speech samples from speakers in different regions and languages, and these samples should have labeled emotional categories. You can use existing voice data sets, such as IEMOCAP, RAVDESS, etc., or record voice samples yourself to build a training set.

Next, the deep learning model can be used for speech emotion recognition. Currently, commonly used models include convolutional neural networks (CNN) and recurrent neural networks (RNN). These models can extract key features in speech and perform emotion classification. When training the model, accent samples can be input together with emotional labels and trained end-to-end.

However, the problem of accent differences is not easy to solve. One possible approach is to use data augmentation techniques to improve model robustness. For example, speed perturbation can be performed on speech samples so that the model can perform good recognition of accents with different rhythms. In addition, the transfer learning method can also be used to use model parameters trained from other speech tasks as initial parameters, and then fine-tune on accent samples. This shortens training time and improves the model's generalization ability.

The following is a simple code example using a convolutional neural network (CNN) for speech emotion recognition:

import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# 定义CNN模型
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(40, 100, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(6, activation='softmax'))

# 编译模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# 加载数据集
X_train = np.load('train_data.npy')
Y_train = np.load('train_labels.npy')
X_test = np.load('test_data.npy')
Y_test = np.load('test_labels.npy')

# 将数据转化为CNN输入的shape
X_train = X_train.reshape(-1, 40, 100, 1)
X_test = X_test.reshape(-1, 40, 100, 1)

# 训练模型
model.fit(X_train, Y_train, batch_size=32, epochs=10, validation_data=(X_test, Y_test))

# 评估模型
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

The above example is a simple convolutional neural network model with an input of 40x100 The speech feature matrix outputs the probabilities of 6 emotional categories. It can be adjusted and improved according to the actual situation.

To sum up, accent differences are a major challenge affecting speech emotion recognition. By constructing a training set containing multiple accent samples and using a deep learning model for training, the problem of accent differences can be solved to a certain extent. At the same time, methods such as data enhancement and transfer learning can also be used to improve the performance of the model. I hope the above content will be helpful in solving the problem of accent differences in speech emotion recognition.

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