> For the complete documentation index, see [llms.txt](https://davidadeola.gitbook.io/influx-ai-whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://davidadeola.gitbook.io/influx-ai-whitepaper/5.0-influx-ai-use-cases-and-applications/5.2-multimodal-technology-framework/5.2.1-ai-assistant-technology-stack.md).
# 5.2.1 AI Assistant Technology Stack
### User Data Collection
1. Portrait data: We use multi-angle portrait acquisition algorithms to collect data from multiple angles.
2. User basic information: We will ask closed and open questions to collect basic information of users;
3. Information collected by crawlers on other Internet platforms: users’ past eating habits, hotel usage data, etc.
### LLM Personalized Learning
The daily recommended outfits will be spoken by the LLM assistant in an anthropomorphic tone.
We will also work with celebrity studios to collect their personalized corpus, voices, and images to achieve highly realistic multimodal video recommendations.
We will introduce fashion KOLs as virtual assistants on a large scale to provide one-on-one customized services.
### Speech synthesis algorithm (TTS)
The implementation process mainly includes the following steps:
**data preparation**
Collect your own speech data set, including speech waveforms and corresponding text; perform preprocessing such as data cleaning, segmentation, and annotation; and divide the data into training sets, development sets, and test sets.
**Acoustic Model**
Use open source models such as Tacotron 2/Transformer TTS; fine-tune models based on proprietary voice data; and customize acoustic models for different languages and speakers.
**Waveform Model**
Use open source waveform generation models such as WaveNet/WaveGlow; fine-tune models based on your own voice data; control voice quality, timbre, speaking style, etc.
**VoCoder**
Use open source vocoder models such as HiFi-GAN/MelGAN; fine-tune models based on your own data; generate high-fidelity speech waveforms.
**Model Ensemble**
Integrate modules such as acoustic models, waveform models, and vocoders; implement end-to-end speech synthesis processes; and consider optimization strategies such as model compression and acceleration.
### Mouth shape algorithm
**data collection**
Recorded video dataset, including the speaker's mouth movements; audio and video time synchronization alignment; data annotation, such as key point coordinates, etc.
**Facial key point detection model**
Use open source FAN/TDDFA and other key point detection models; retrain based on own video datasets; improve the accuracy of mouth and lip key point detection.
**Mouth shape classification/regression model**
Use open source DrSample/LRW and other mouth shape models; combine with own mouth shape annotation data to train the model; support two modes: mouth shape classification and coordinate regression.
**Audiovisual fusion model**
Use open source AV-HuBERT and other audio-visual fusion models; integrate visual mouth shape and speech features; and improve the accuracy of mouth shape animation restoration.
**Model deployment**
Integrate various modules to produce end-to-end lip animation; deploy to servers or embedded devices; consider factors such as real-time performance and computing resources.
### User data encryption and storage
**Data Classification and Encryption**
Classify user data according to its sensitivity; use different encryption algorithms and key encryption, such as AES, RSA, etc.; sensitive data such as identity information, account passwords, etc. require high-intensity encryption.
**Key Management**
Securely store and manage keys; use devices such as hardware security modules (HSM); support key lifecycle management and key backup and recovery.
**Database storage**
The encrypted data is stored in the database system; select a suitable database such as MySQL, MongoDB, etc. according to the data volume and access mode; implement necessary access control and audit strategies.
**Secure transmission**
Protect data during network transmission; use secure channels such as HTTPS and VPN for transmission; implement security measures such as firewalls and intrusion detection.
**Data desensitization and anonymization**
Delete or mask sensitive data in scenarios such as data analysis; for example, use hash algorithms, pseudonyms, etc. to achieve anonymization.
**Data backup and disaster recovery**
Back up important data regularly; develop a data disaster recovery plan; backup data also needs to be stored in encrypted form.
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