# Big Data and AI-Driven Product Design: A Survey
## 4 Product Design Based on Big Data and AI
### Product Design Based on Audio Data  
Big audio data [219] refer to information in the form of sound or voice. The customer center is a valuable resource for collecting users' complaints, inquiries, and suggestions throughout the product service cycle [220]. Audio feedback can be utilized to collect user requirements, provide recommendations, evaluate products, and improve product design. However, there is currently a lack of literature on product design based on big audio data. Therefore, we will explore several key AI technologies that could be employed in the future, such as speech recognition, speaker identification, and emotion recognition. Some studies have opted to manually record telephone complaints as text to avoid direct processing of audio signals [221; 222]. However, this approach heavily relies on the recorder and is a labor-intensive and time-consuming process. In comparison, speech recognition can accomplish this task automatically [223]. Speech recognition, also referred to as automatic speech recognition (ASR), computer speech recognition, and speech-to-text, involves converting human speech into computer-readable information, typically in the form of text, although it may also be binary codes or character sequences [224]. The process of speech recognition is shown in Figure 14. Speech recognition is widely used in mobile communication, search engines, human-computer interaction, among other applications [225]. Figure 14: The process of speech recognition. Framing the input speech into many frames and transfer the waveform to extract feature vectors. The acoustic model is then used to convert features into phonemes, matched to words through the pronunciation dictionary. Finally, it eliminates the confusion of homophones by the language model. Speaker identification, also referred to as voiceprint recognition, is a technology that identifies individuals based on their speech. Each person's voice has unique characteristics, which are determined by two factors: the size of the sound cavity and how the vocal organs are manipulated. Like fingerprints, voiceprints need to be collected and stored in a database prior to analysis. The spectrogram is generated by the amplitude of the short-time Fourier transform (STFT) of the audio signal (Figure 15) [226]. Voiceprint recognition is achieved by extracting speaker parameters, such as pitch frequency and formant, and using machine learning methods and AI algorithms. Voiceprint recognition has been widely applied in various fields, including biometric authentication, crime forensics, mobile payment, and social security. However, the variability of voiceprint, the differences in audio acquisition equipment, and environmental noise interference pose significant challenges for the voiceprint recognition task. As a vital division of audio processing and emotion computing, speech emotion recognition aims to identify the emotions expressed by speaker [227], including but not limited to, anger, sadness, surprise, pleasure, and panic. This process can be regarded as a classification problem, and selecting the appropriate emotion feature is essential for its success [228; 229]. We have summarized commonly used acoustic parameters in Table 4. Moreover, since spectrograms are images, CNN can be used to automatically learn features for speech recognition, speaker identification, and emotion recognition [230]. As AI products gain popularity, there has been increasing focus on audio signal processing algorithm, particularly in the areas of speech recognition and emotion recognition. These algorithms directly impact the user experience and play a crucial role in customer decision-making. Despite the enormous potential of audio data in product design,