# Hybrid LSTM-Attention and CNN Model for Enhanced Speech Emotion Recognition
## 3 Proposed Model
### Data Augmentation  
Data augmentation strategies are implemented to ensure that the datasets mirror real-world audio scenarios as closely as possible. Thus, we enhance the robustness of the model, making it more resilient to diverse and unforeseen inputs. These strategies employ a sequence of meticulous steps that both diversify and enrich the primary dataset. These modifications not only increase the volume of data available for training but also introduce variations that simulate potential real-world challenges, ensuring that the model is well-prepared for a range of audio conditions. This is achieved through a series of steps that modify and enhance the original data, as follows:  
Time Stretching: This particular augmentation technique focuses on manipulating the length or duration of the audio clip without affecting its inherent pitch or tonal quality. By stretching or compressing the audio, we introduce variations in the temporal domain, effectively simulating scenarios where speech or sound patterns may be occur more quickly or slowly than usual. By integrating time-stretched versions into the dataset, the model becomes better equipped to recognize and process audio across a broader spectrum of speech tempos, thereby enhancing its versatility and applicability in diverse real-world scenarios.  
Pitch Shifting: This technique revolves around modifying the tonal quality or pitch of the audio while keeping its length or duration constant. By adjusting the pitch upwards or downwards, we introduce a range of tonal variations, effectively mimicking situations in which vocal frequencies might vary, such as differences in age, mood, or individual vocal characteristics. Such changes are fundamental because people naturally possess varied voice pitches, from deep bass voices to high-pitched tones.  
Adding Noise: This technique involves the intentional injection of random noise, typically Gaussian noise, into the audio signal. By introducing this element of randomness, the audio data becomes more representative of real-world scenarios where background disturbances, interference, or imperfect recording conditions might exist. Such a method is crucial, as it prepares the model to function effectively even in less-than-ideal audio environments. By training with noised-enhanced data, the model develops resilience against disturbances and can more accurately process and classify audio, even when faced with unpredictable or suboptimal recording conditions commonly encountered in real-world situations.  
Time Shifting: This technique involves moving the audio waveform along the time axis, typically by a set duration like 1 s. By doing so, the starting point of the audio changes, simulating scenarios in which the onset of a sound or speech might be earlier or later than expected. Such adjustments are essential as they introduce temporal variations into the dataset, mimicking potential real-world misalignments or offsets in audio recordings.  
Table 1 provides detailed information on both the original and augmented counts of features such as ZCR, RMS, and MFCC within the dataset. This table serves as a comprehensive reference, showcasing the depth and range of data available, both in their untouched form and after the enhancement process through augmentation. By presenting both the pure and augmented data, researchers and analysts can gain a better understanding of the dataset's structure, richness, and the extent to which data augmentation techniques have been applied to enhance its comprehensiveness.