About product
Voice2AI is an innovative project specializing in the development and implementation of artificial intelligence (AI) and voice technologies. The company’s main goal is to create solutions that help automate processes, improve customer interactions, and increase business efficiency. These solutions include speech recognition (ASR), speech synthesis (TTS), as well as natural language understanding and analysis systems (NLU/NLP).
Key products of the company:
Voice AI: VoIP Call 2.0 — a cloud-based SaaS solution that provides notifications via Telegram or WhatsApp about incoming calls containing specific keywords or phrases, as well as alerts about calls where these keywords are absent.
Voice AI: My Office — a software suite that integrates with smart speakers like Siri, Google Assistant, and Alexa. Your AI assistant analyzes all conversations in the office, sales floor, or workshop and immediately reports on conflict situations, complaints, rude behavior from employees, or conversely, successful deals, positive customer emotions, and expressions of gratitude. It features a flexible filtering system based on keywords and parameters.
Voice AI: My Home — integration with smart speakers such as Siri, Google Assistant, and Alexa. Your AI assistant monitors all sounds in an apartment or country house and promptly alerts you to the appearance of unfamiliar voices, trigger words or phrases, arguments, and more. It can start audio recording upon your command. The system also offers customizable filters based on keywords and parameters.
Technologies used by Voice AI The technological solutions for speech recognition and conversation analysis involve several key components and methods that enable converting spoken language into text and then finding specific words or phrases within it. The main technologies include:
1. Automatic Speech Recognition (ASR): This technology transforms audio signals into text. Modern ASR systems utilize deep neural networks such as recurrent neural networks (RNNs), transformers, or their combinations to interpret speech accurately even in noisy environments. They are trained on vast datasets to achieve high recognition precision.
1. Natural Language Processing (NLP): After converting speech to text, NLP techniques are employed to understand the meaning and structure of the text. This involves parsing sentences, extracting key words, and determining context.
2. Keyword and phrase search & filtering: During text analysis, algorithms search for patterns or specific keywords using regular expressions, morphological analysis (considering word forms), or more advanced machine learning models to identify relevant phrases or emotional tones.
3. Classification and machine learning models: To assess the importance or category of statements—such as complaints, gratitude, conflicts—machine learning classifiers like neural network-based models or gradient boosting algorithms are used. They help automatically highlight significant phrases or situations.
4. Integration with notification systems: Processed text can trigger automatic alerts or actions—for example, when a certain phrase is detected, the system can notify a manager or log the event in a journal.
These technological solutions enable not only accurate speech-to-text conversion but also efficient identification of key words and phrases within conversations for further analysis or automated responses.