Emotion distribution issues in speech emotion recognition technology

The problem of emotion distribution in speech emotion recognition technology requires specific code examples

In the fields of human-computer interaction and intelligent speech applications, speech emotion recognition technology is widely used. Because speech is one of the main ways for humans to express emotions, through emotional analysis of speech signals, users' emotional needs can be better understood and responded to. However, there is an important problem in speech emotion recognition, namely the emotion distribution problem.

The emotion distribution problem refers to the imbalance in the number of samples of different emotion categories in the data set in the speech emotion recognition task. In real-life data sets, the sample distribution of various emotion categories is often uneven, and the number of samples of some emotion categories far exceeds that of other emotion categories. In this case, traditional classification algorithms may be biased towards the majority category, resulting in poor emotion recognition for minority categories.

In order to solve the problem of emotion distribution, the following methods can be used:

1. Data Augmentation

Data augmentation is a commonly used method to solve the problem of emotional distribution. Methods to balance data distribution. By copying or performing some transformation operations on minority category samples, the number of samples is increased, thereby making the number of samples of different emotional categories more balanced. Specifically, in the speech emotion recognition task, you can consider performing operations such as speed change, noise reduction, and translation on audio data with fewer emotional categories to increase the number of samples in the minority categories.

Sample code:

import librosa import numpy as np # 加载原始音频数据 audio_data, sr = librosa.load('audio.wav', sr=None) # 数据增强 augmented_data = [] # 变速操作，速度增加20% speed_factor = 1.2 augmented_data.append(librosa.effects.time_stretch(audio_data, speed_factor)) # 降噪操作，使用小波降噪算法 augmented_data.append(librosa.effects.decompose(audio_data)) # 平移操作，时间向后平移2s shift_value = int(sr * 2) augmented_data.append(np.roll(audio_data, shift_value)) # 存储增强后的音频数据 for idx, augmented_audio in enumerate(augmented_data): librosa.output.write_wav(f'augmented_audio_{idx}.wav', augmented_audio, sr)

2. Resampling

Resampling is a method of changing the number of samples, through upsampling or downsampling To adjust the proportion of the number of samples of each category in the data set. In the emotion distribution problem, resampling can be used to adjust the number of samples in the minority category so that it is close to the number of samples in the majority category, thereby reducing the difference in the number of category samples.

Sample code:

from sklearn.utils import resample # 样本重采样 resampled_data = [] # 将少数类别样本数量调整为多数类别样本数量 majority_samples = data[data['label'] == 'majority_label'] minority_samples = data[data['label'] == 'minority_label'] resampled_minority_samples = resample(minority_samples, n_samples=len(majority_samples)) resampled_data = pd.concat([majority_samples, resampled_minority_samples]) # 使用重采样后的样本训练分类模型

Through the two methods of data enhancement and resampling, the emotional distribution problem in speech emotion recognition can be effectively solved and the accurate recognition rate of minority categories of emotions can be improved. However, the specific operations and parameters of the method need to be adjusted according to the actual situation to obtain the best recognition effect. At the same time, methods such as feature selection and model tuning can be further comprehensively considered to improve the performance and stability of speech emotion recognition technology.

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